Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

In summary, our contributions are as follow:1. We are the first to introduce copy-paste as a synthesis technique to address partially-labeled images for incremental segmentation.

The review comment to be evaluated is:

Strengths:
- The paper introduces the copy-paste data augmentation strategy to the few-shot incremental semantic segmentation, which seems to be a novel combination.
- The proposed guided sample selection method for memory construction and data synthesis improves the performance of several baseline methods.
- The paper is most clearly written and easy to follow.

Weaknesses:
- While the proposed data augmentation strategy helps few-shot learning, its technical novelty in the incremental semantic segmentation (ISS) is rather limited, as it is an add-on module and has to rely on the base ISS methods.
- The construction of the memory-replay buffer has several limitations and will affect the quality of the data augmentation in ISS:
1) Only the base stage has fully-annotated images that can be used for the target images in the copy-paste operation, and all the images from the incremental stages are partially labeled, which are unusable for that purpose. As a result, when the incremental learning evolves, the memory has to add samples of new classes and remove some base-stage images due to the capacity limit. Eventually, the memory buffer would be less useful.
2) The diversity-guided selection procedure in Sec 3.2 considers each class separately in sampling. However, for semantic segmentation, it is common that each image has regions of multiple classes. It is unclear how such a method can balance different classes. This is also a problem for the class-frequency-guided selection, as each image can have multiple classes. Another issue is that this method did not take into account the region sizes as it only considers the image-level sampling factor.
- There are several concerns on the experimental evaluation, which make the conclusion less convincing:
1) Both benchmarks are mainly used in the few-shot segmentation literature, which are a bit small in terms of data set size and number of classes. Similar to SSUL and other ISS works, the method should be evaluated on more diverse ISS task settings and larger dataset, such as ADE.
2) For the few-shot side, while this work adopts a different evaluation protocol (as described in Sec. 4.2), it would be more informative if it also uses the protocol in Cermelli et al 2021 for a fair comparison. Moreover, unlike the standard ISS, the training process of the few-shot setting typically has a large variance due to different training samples annotated. Thus it should report both the mean and standard deviation in the final results.
3) The gaps in the ablation study are relatively small. It would more convincing if the standard deviation can be provided. It is unclear how significant such improvements are in the few-shot learning setting.

The evidence to be evaluated is:Noticeably, a few attempts have been made by recent work to address the semantic background shift problem proposed by Cermelli et al. (2020) via either pseudo-labeling (Cha et al., 2021; Douillard et al., 2021; Yan et al., 2021) or modified loss (Cermelli et al., 2020; Zhang et al., 2022) to train on partially-annotated images of novel classes. However, existing work relies on rich annotations and tends to fail when only a limited amount of data is available. In contrast, our work enables incremental segmentation learning with _few data_ via a guided copy-paste process, which demonstrates promising performance under the few-shot and more impoverished single-instance setting.

Copy-and-paste is an augmentation technique that copies a subset of objects from one image and pastes onto the other image using their segmentation masks. Many works (Dwibedi et al., 2017; Ghiasi et al., 2021; Dvornik et al., 2018) have been done to investigate how copy-and-paste augmentation can help with various visual tasks.

We extend the augmentation strategy from Ghiasi et al. (2021) and construct an intuitive baseline called Naive copy-Paste Synthesis (NPS) to adapt it to our online task setting.

In our work, we propose a series of techniques to guide the copy-paste synthesizer to address these gaps, whose effectiveness is evident from the significant improvement from NPS.

It is a _generic and model-agnostic_ data synthesis framework that generates fully-labeled scenes from partially-annotated images of novel objects as a preprocessor to the underlying segmentation model.

During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\).

In principle, GAPS is model-agnostic, which means that it can work with many incremental segmentation models as a diverse data source to improve their performance.

We hypothesize that synthesizing novel objects onto contextually consistent base images would result in an improved learning process. For example, a TV in an apartment should more likely be pasted onto an image of another apartment rather than an outdoor landscape. We design a context-guided sampling algorithm to select images from \(\hat{D_{0}}\) that are contextually similar to the provided partially-labeled images.

Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now.

In Table 2, we ablate guidance designs in GAPS to illustrate how different types of guidance contribute to the final incremental learning performance than naive copy-paste synthesis.

We take a closer look at step-wise performance (whose visualization is available in Appendix A.11) and found that the reason is due to unguided copy-paste's oversampling of novel classes that are being adapted, and forgetting of classes learned in the previous incremental learning stage and not in the memory-replay buffer.

Here we consider a more challenging task setting, which we term _single-instance incremental learning_. Namely, for training images provided in incremental learning stages, if there are multiple instances of a novel class in the image, we assume that only _one instance_ will be annotated. To simulate this setting, we use the instance-level segmentation annotation provided by the COCO dataset to enforce only annotation of one novel instance in every image is available to the model. Since state-of-the-art incremental segmentation approaches use pseudo-labeling (Cha et al., 2021), we design a method PIFS(\(\mathcal{L}_{2}\))\({}^{\dagger}\), which simulates combining PIFS(\(\mathcal{L}_{2}\)) with pseudo-labeling to cope with partially-annotated sample.

This highlights the potential of copy-paste synthesis as an alternative to the existing pseudo-labeling paradigm to cope with the more realistic single-instance setting.

In this paper, we demonstrate how judicious use of copy-paste dramatically boosts the performance of incremental segmentation methods under the few-shot setting and enables learning with partially-annotated images. Our proposed GAPS technique selects representative exemplars in the memory-replay buffer and addresses the problems of class imbalance and contextual mismatch in synthesis.

We believe that copy-paste can serve as a promising alternative to pseudo-labeling and modified loss to enable learning on partially-annotated images.

We investigate how different probabilistic strategies to apply copy-and-paste augmentation affects GAPS.

For every selected base image, the _Aways Paste_ method randomly picks a novel category and always synthesizes an instance of the selected novel category onto the given image. Finally, the _50-50-Paste_ method is an extension of the naive copy-paste method to leverage past novel samples like vRFS. Essentially, it replays samples from learned novel classes with 50% probability, and uses novel class from the current incremental learning task also with 50% probability.

Fig. 5 shows qualitative results of PIFS(\(\mathcal{L}_{2}\)), PIFS(\(\mathcal{L}_{2}\))+NPS (naive copy-paste), and PIFS(\(\mathcal{L}_{2}\))+GAPS on the PASCAL-5-1 split dataset under the 1-shot setting using images on the left column. We can see that PIFS and PIFS+NPS gives reasonably good predictions on classes that are adapted recently (e.g., the cow class in the last row). However, they tend to exhibit catastrophic forgetting for classes learned earlier (e.g., the bus example in PIFS). On the other hand, the model trained using GAPS not only yields good result on recently learned classes, but also maintains promising performance on classes adapted earlier.

Input: Table 2: Ablation study of components in GAPS on PIFS(L2) on the COCO-20i dataset under 5- shot setting. Note that when only combined with the memory-replay buffer, the base IoU is higher because model has access to additional full annotations. When diversity guidance (D-guide) is disabled, D̂0 consists of random examples from the base dataset, resulting in worse base performance. When context guidance (C-guide) is disabled, a base image is uniformly sampled. When frequency guidance (F-guide) is disabled, a novel instance is sampled uniformly and is always pasted onto

Input: Figure 1: Our proposed method utilizes guided copy-paste augmentation to synthesize diverse training data, using as few as one single novel instance for training. For example, the model encounters an image of many motorcycles, which is novel to the model. As a result, the model incorrectly assigns learned bicycle labels to these pixels and therefore needs to be updated. Our proposed method can adapt to the novel motorcycle class with an annotation of a single motorcycle, which can be efficiently annotated; whereas previous work (Cermelli et al., 2020; 2021) require time-consuming annotation of all instances of motorcycles or even the entire image. Best view in color.

Input: Figure 7: Multi-step qualitative results of our method (GAPS) on the PASCAL-5-1 split under 1- shot setting. (Best view in color.)

Input: Figure 2: Overview of GAPS. During the incremental learning stage, GAPS takes in as few as one annotated instance of a single image. It is more probable for GAPS to select a scene contextually similar to the provided image from memory-replay buffer D̂0. The image is then probabilistically pasted to generate synthetic fully-labeled scenes. Note that GAPS is model-agnostic, and here we use PIFS (Cermelli et al., 2021) as an example for the underlying segmentation model to illustrate how GAPS is applied as a pre-processor. Best seen in color.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim, evidence, and reviewer comments, I find the claim "We are the first to introduce copy-paste as a synthesis technique to address partially-labeled images for incremental segmentation" to be somewhat overstated.

While the evidence shows the authors implement copy-paste in an incremental segmentation context, and reviewer comments acknowledge this as a "novel combination," they also note the "technical novelty is rather limited" since it's an "add-on module." The presented evidence reveals previous work on copy-paste augmentation techniques (Dwibedi et al., 2017; Ghiasi et al., 2021; Dvornik et al., 2018), suggesting the core technique itself isn't novel. The authors extend existing methods rather than introducing copy-paste as an entirely new concept.

<score>4</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Despite the great success of copy-paste synthesis in the conventional _offline_ visual recognition, we demonstrate substantially degraded performance of its naive extension in our _online_ scenario, due to newly encountered challenges.

The evidence to be evaluated is:It is known that many learning-based models suffer from catastrophic forgetting (McCloskey and Cohen, 1989), a pheonomenon that causes models to perform significantly worse on old tasks when they are fine-tuned to adapt to new tasks. _Incremental learning_ studies how to enable models to adapt to new classes while mitigating catastrophic forgetting without accessing the old dataset or full-scale re-training. This problem has been studied extensively in image classification (Li and Hoiem, 2017; Lopez-Paz and Ranzato, 2017; Yoon et al., 2018; Rebuffi et al., 2017; Mallya et al., 2018; Castro et al., 2018; Bang et al., 2021); whilst relatively less work have been done to study incremental learning under the task setting of semantic segmentation (Cermelli et al., 2020; Michieli and Zanuttigh, 2019; Cha et al., 2021; Douillard et al., 2021; Zhang et al., 2022).

However, existing work relies on rich annotations and tends to fail when only a limited amount of data is available. In contrast, our work enables incremental segmentation learning with _few data_ via a guided copy-paste process, which demonstrates promising performance under the few-shot and more impoverished single-instance setting.

In comparison, our proposed method works in a more challenging and realistic setting where both base classes and novel classes need to be segmented.

While there are many works in few-shot incremental image classification (Tao et al., 2020; Cheraghian et al., 2021), relatively fewer works have been done to investigate few-shot incremental segmentation (Tian et al., 2020; Cermelli et al., 2021; Ganea et al., 2021).

Unlike Tian et al. (2020a), which only performs a single update of weights in the classifier, PIFS (Cermelli et al., 2021) apply regularization techniques to allow fine-tuning of the entire network, achieving state-of-the-art result in few-shot incremental semantic segmentation. However, PIFS (Cermelli et al., 2021) is fine-tuned on only a small number of samples, which leads to sub-optimal performance due to overfitting.

Many works (Dwibedi et al., 2017; Ghiasi et al., 2021; Dvornik et al., 2018) have been done to investigate how copy-and-paste augmentation can help with various visual tasks. Dvornik _et al._ apply copy-paste augmentation in object detection by designing a neural network to consider context and guide copy-paste. However, the context guidance method proposed by Dvornik _et al._ can not be trivially applied to our application since it requires abundant fully-annotated training data. More recently, Ghiasi _et al._ conduct extensive experiments to demonstrate the effectiveness of simple copy-paste in the instance segmentation problem. We extend the augmentation strategy from Ghiasi et al. (2021) and construct an intuitive baseline called Naive copy-Paste Synthesis (NPS) to adapt it to our online task setting. However, as we will demonstrate, such naive adaptation gives unsatisfactory performance in our task setting because of _gaps_ between the static offline learning and continual online learning. In our work, we propose a series of techniques to guide the copy-paste synthesizer to address these gaps, whose effectiveness is evident from the significant improvement from NPS.

It is a _generic and model-agnostic_ data synthesis framework that generates fully-labeled scenes from partially-annotated images of novel objects as a preprocessor to the underlying segmentation model.

During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\). The strategy by which we choose base exemplars and novel segments is _context-guided_ (Section 3.3) and _class-frequency-guided_ (Section 3.4).

We hypothesize that synthesizing novel objects onto contextually consistent base images would result in an improved learning process.

We design a context-guided sampling algorithm to select images from \(\hat{D_{0}}\) that are contextually similar to the provided partially-labeled images. GAPS uses a scene embedding network \(h:\mathcal{X}\rightarrow\mathbb{R}^{m}\) to estimate pairwise contextual similarity between any two given images. The embedding \(h\) maps an input image to a scene embedding vector in a metric space where pairwise similarity comparison between two embeddings are possible. In our system, we use an off-the-shelf VGGNet (Simonyan and Zisserman, 2015) and replace its last fully connected layer with a cosine-similarity-based classifier. The network \(h\) is trained using the Places365 (Zhou et al., 2017) dataset, which contains \(365\) scene categories and roughly 1.6 million images in its training split. During training, the scene prediction is done by comparing the predicted embedding with existing prototypes.

As one extreme, one can follow Ghiasi et al. (2021) and always apply copy-paste augmentation to paste novel objects onto every base image. However, this will lead to oversampling of novel categories in the current task, which we found to hurt the performance of existing classes. On the other hand, rarely pasting novel instances would lead to undersampling of the novel class. Therefore, to guide copy-paste in the online setting, we design a synthesis strategy called vRFS based on RFS (Repeat Factor Sampling) described by Gupta et al. (2019) to perform synthesis.

If \(c\in\mathcal{C}_{j}\) with \(j\geq 1\), we first compute its class frequency by \(f_{c}=nShot/|\hat{D}_{0}|\), where \(nShot\) denotes the number of images in \(D_{j}\) with at least one pixel of \(c\). Then, the category-wise sampling factor for \(c\) is given by \(r_{c}=\textsc{Max}(1,\sqrt{t/f_{c}})\). Note that in (Gupta et al., 2019), \(t\) is chosen as a hyperparameter to be tuned. However, we empirically found that setting \(t\) to be the multiplicative inverse of total number of classes, or \(t=1/|\cup_{1,...,j}Y_{j}|\), is enough to yield stable results across different datasets and under different few-shot settings. This eliminates the need to search a hyperparameter for different settings and make our proposed method more robust towards different task settings. During the synthesis process, we first randomly select a novel class \(c_{N}\) from \(\mathcal{C}_{j}\), and another class \(c_{o}\) from \(\cup_{1,...,j}C_{j}\setminus\{c_{N}\}\).

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories. In Table 2, we ablate guidance designs in GAPS to illustrate how different types of guidance contribute to the final incremental learning performance than naive copy-paste synthesis.

We take a closer look at step-wise performance (whose visualization is available in Appendix A.11) and found that the reason is due to unguided copy-paste's oversampling of novel classes that are being adapted, and forgetting of classes learned in the previous incremental learning stage and not in the memory-replay buffer.

Here we consider a more challenging task setting, which we term _single-instance incremental learning_. Namely, for training images provided in incremental learning stages, if there are multiple instances of a novel class in the image, we assume that only _one instance_ will be annotated. To simulate this setting, we use the instance-level segmentation annotation provided by the COCO dataset to enforce only annotation of one novel instance in every image is available to the model.

We can observe that the pseudo-labeling baseline, \(\text{PIFS}(\mathcal{L}_{2})^{\dagger}\), yields substantially worse performance when the model receives single-instance annotations. We reason this is due to noisy labels generated by the pseudo-labeling process, where novel instances are incorrectly labeled as background.

This highlights the potential of copy-paste synthesis as an alternative to the existing pseudo-labeling paradigm to cope with the more realistic single-instance setting.

In this paper, we demonstrate how judicious use of copy-paste dramatically boosts the performance of incremental segmentation methods under the few-shot setting and enables learning with partially-annotated images.

We experimented with all of these variants and found that the diversity-aware construction yields the best result in all metrics. Compared to randomly selected samples or easy samples, using hard samples result in better performance on novel categories. On the other hand, we found that using a mixture of easiest samples and hardest sample is also a good strategy, which achieves superior performance in base categories compared to other baselines, and comparable performance to our diversity-aware sampling. It is also worth noting that both the 'GreedyClass' method and the 'BalancedRegion' method achieve almost comparable results to our methods, but still slightly underperform our diversity guidance. We investigate how different probabilistic strategies to apply copy-and-paste augmentation affects GAPS.

For every selected base image, the _Aways Paste_ method randomly picks a novel category and always synthesizes an instance of the selected novel category onto the given image. Finally, the _50-50-Paste_ method is an extension of the naive copy-paste method to leverage past novel samples like vRFS.

In the result, we can observe that our method (vRFS) achieves the best result in terms of the novel IoU and harmonic mean of base and novel IoU. On the other hand, the 50-50-Paste baseline and the CAS baseline achieves better results possibly because they undersample novel classes.

We observe a steady increase in both the base IoU and novel IoU in this challenging task setting on methods augmented with GAPS.

We can observe that storing samples observed during the incremental learning stage leads to only moderate increase in novel IoU. Our PIFS(\(\mathcal{L}_{2}\))+GAPS significantly outperforms both baselines.

Fig. 5 shows qualitative results of PIFS(\(\mathcal{L}_{2}\)), PIFS(\(\mathcal{L}_{2}\))+NPS (naive copy-paste), and PIFS(\(\mathcal{L}_{2}\))+GAPS on the PASCAL-5-1 split dataset under the 1-shot setting using images on the left column. We can see that PIFS and PIFS+NPS gives reasonably good predictions on classes that are adapted recently (e.g., the cow class in the last row). However, they tend to exhibit catastrophic forgetting for classes learned earlier (e.g., the bus example in PIFS). On the other hand, the model trained using GAPS not only yields good result on recently learned classes, but also maintains promising performance on classes adapted earlier.

Input: Table 2: Ablation study of components in GAPS on PIFS(L2) on the COCO-20i dataset under 5- shot setting. Note that when only combined with the memory-replay buffer, the base IoU is higher because model has access to additional full annotations. When diversity guidance (D-guide) is disabled, D̂0 consists of random examples from the base dataset, resulting in worse base performance. When context guidance (C-guide) is disabled, a base image is uniformly sampled. When frequency guidance (F-guide) is disabled, a novel instance is sampled uniformly and is always pasted onto

Input: Figure 3: Long-term continual learning performance of PIFS(L2) and PIFS(L2)+GAPS on the COCO-20-1 split under 5-shot setting. Best view in color.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on my review, the claim "Despite the great success of copy-paste synthesis in the conventional offline visual recognition, we demonstrate substantially degraded performance of its naive extension in our online scenario, due to newly encountered challenges" is well-stated and supported by multiple pieces of evidence.

The evidence shows:
1. Table 2 demonstrates that naive copy-paste without guidance performs worse than the full GAPS system
2. The text explicitly discusses how "naive adaptation gives unsatisfactory performance" and explains the specific challenges
3. Figure 3 illustrates the performance degradation in the online/continual learning scenario
4. The authors provide detailed experimental results showing why unguided copy-paste leads to problems like oversampling of novel classes

The claim accurately reflects the evidence without exaggeration, acknowledging both the success in offline settings and the challenges in online scenarios.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Despite the great success of copy-paste synthesis in the conventional _offline_ visual recognition, we demonstrate substantially degraded performance of its naive extension in our _online_ scenario, due to newly encountered challenges.

The review comment to be evaluated is:

Strengths:
1. The paper investigates a novel and understudied scenario that is more realistic than earlier benchmarks.
2. The study gives a comprehensive summary of similar works as well as comparisons and contrasts between them.
3. The study expands the present benchmark with new experiments involving only one labeled instance of the new classes, demonstrating that the GAPS can address the issue of missing annotations.

Weaknesses:
1. The ablation study may be extended to other settings and may provide more insights about the paper choices. In particular, it may start from a baseline that uses exemplars without pasting the new classes on them, to show that pasting is important to improve performance and that the gain is not only given by the regularization effect of using exemplars (as proposed by GDumb [a]). Moreover, the paper should clarify if, while disabling the diversity guidance, the exemplars are sampled uniformly for the old classes or if they are randomly sampled on the full dataset.
2. From the ablation study, context sampling appears to marginally improve the results. Moreover, it is not clear how the performance will change by using a pre-training dataset different from Places-365, such as ImageNet or ADE20K. Furthermore, can it be trained directly on the base dataset of the considered setting or can the method use the segmentation network itself (for example, using an auxiliary classifier on the encoder features)?
3. The paper baselines appear to be very naive. In particular, the paper reports the results for Fine-Tuning, MiB, and PIFS without considering exemplars, introducing an unfair comparison with respect to GAPS. The baselines should be reported both without exemplars (as in the original paper) and with them (indeed, without the proposed copy-and-paste strategy).

[a] Prabhu, Ameya, Philip HS Torr, and Puneet K. Dokania. "Gdumb: A simple approach that questions our progress in continual learning." European conference on computer vision. Springer, Cham, 2020.

The evidence to be evaluated is:It is known that many learning-based models suffer from catastrophic forgetting (McCloskey and Cohen, 1989), a pheonomenon that causes models to perform significantly worse on old tasks when they are fine-tuned to adapt to new tasks. _Incremental learning_ studies how to enable models to adapt to new classes while mitigating catastrophic forgetting without accessing the old dataset or full-scale re-training. This problem has been studied extensively in image classification (Li and Hoiem, 2017; Lopez-Paz and Ranzato, 2017; Yoon et al., 2018; Rebuffi et al., 2017; Mallya et al., 2018; Castro et al., 2018; Bang et al., 2021); whilst relatively less work have been done to study incremental learning under the task setting of semantic segmentation (Cermelli et al., 2020; Michieli and Zanuttigh, 2019; Cha et al., 2021; Douillard et al., 2021; Zhang et al., 2022).

However, existing work relies on rich annotations and tends to fail when only a limited amount of data is available. In contrast, our work enables incremental segmentation learning with _few data_ via a guided copy-paste process, which demonstrates promising performance under the few-shot and more impoverished single-instance setting.

In comparison, our proposed method works in a more challenging and realistic setting where both base classes and novel classes need to be segmented.

While there are many works in few-shot incremental image classification (Tao et al., 2020; Cheraghian et al., 2021), relatively fewer works have been done to investigate few-shot incremental segmentation (Tian et al., 2020; Cermelli et al., 2021; Ganea et al., 2021).

Unlike Tian et al. (2020a), which only performs a single update of weights in the classifier, PIFS (Cermelli et al., 2021) apply regularization techniques to allow fine-tuning of the entire network, achieving state-of-the-art result in few-shot incremental semantic segmentation. However, PIFS (Cermelli et al., 2021) is fine-tuned on only a small number of samples, which leads to sub-optimal performance due to overfitting.

Many works (Dwibedi et al., 2017; Ghiasi et al., 2021; Dvornik et al., 2018) have been done to investigate how copy-and-paste augmentation can help with various visual tasks. Dvornik _et al._ apply copy-paste augmentation in object detection by designing a neural network to consider context and guide copy-paste. However, the context guidance method proposed by Dvornik _et al._ can not be trivially applied to our application since it requires abundant fully-annotated training data. More recently, Ghiasi _et al._ conduct extensive experiments to demonstrate the effectiveness of simple copy-paste in the instance segmentation problem. We extend the augmentation strategy from Ghiasi et al. (2021) and construct an intuitive baseline called Naive copy-Paste Synthesis (NPS) to adapt it to our online task setting. However, as we will demonstrate, such naive adaptation gives unsatisfactory performance in our task setting because of _gaps_ between the static offline learning and continual online learning. In our work, we propose a series of techniques to guide the copy-paste synthesizer to address these gaps, whose effectiveness is evident from the significant improvement from NPS.

It is a _generic and model-agnostic_ data synthesis framework that generates fully-labeled scenes from partially-annotated images of novel objects as a preprocessor to the underlying segmentation model.

During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\). The strategy by which we choose base exemplars and novel segments is _context-guided_ (Section 3.3) and _class-frequency-guided_ (Section 3.4).

We hypothesize that synthesizing novel objects onto contextually consistent base images would result in an improved learning process.

We design a context-guided sampling algorithm to select images from \(\hat{D_{0}}\) that are contextually similar to the provided partially-labeled images. GAPS uses a scene embedding network \(h:\mathcal{X}\rightarrow\mathbb{R}^{m}\) to estimate pairwise contextual similarity between any two given images. The embedding \(h\) maps an input image to a scene embedding vector in a metric space where pairwise similarity comparison between two embeddings are possible. In our system, we use an off-the-shelf VGGNet (Simonyan and Zisserman, 2015) and replace its last fully connected layer with a cosine-similarity-based classifier. The network \(h\) is trained using the Places365 (Zhou et al., 2017) dataset, which contains \(365\) scene categories and roughly 1.6 million images in its training split. During training, the scene prediction is done by comparing the predicted embedding with existing prototypes.

As one extreme, one can follow Ghiasi et al. (2021) and always apply copy-paste augmentation to paste novel objects onto every base image. However, this will lead to oversampling of novel categories in the current task, which we found to hurt the performance of existing classes. On the other hand, rarely pasting novel instances would lead to undersampling of the novel class. Therefore, to guide copy-paste in the online setting, we design a synthesis strategy called vRFS based on RFS (Repeat Factor Sampling) described by Gupta et al. (2019) to perform synthesis.

If \(c\in\mathcal{C}_{j}\) with \(j\geq 1\), we first compute its class frequency by \(f_{c}=nShot/|\hat{D}_{0}|\), where \(nShot\) denotes the number of images in \(D_{j}\) with at least one pixel of \(c\). Then, the category-wise sampling factor for \(c\) is given by \(r_{c}=\textsc{Max}(1,\sqrt{t/f_{c}})\). Note that in (Gupta et al., 2019), \(t\) is chosen as a hyperparameter to be tuned. However, we empirically found that setting \(t\) to be the multiplicative inverse of total number of classes, or \(t=1/|\cup_{1,...,j}Y_{j}|\), is enough to yield stable results across different datasets and under different few-shot settings. This eliminates the need to search a hyperparameter for different settings and make our proposed method more robust towards different task settings. During the synthesis process, we first randomly select a novel class \(c_{N}\) from \(\mathcal{C}_{j}\), and another class \(c_{o}\) from \(\cup_{1,...,j}C_{j}\setminus\{c_{N}\}\).

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories. In Table 2, we ablate guidance designs in GAPS to illustrate how different types of guidance contribute to the final incremental learning performance than naive copy-paste synthesis.

We take a closer look at step-wise performance (whose visualization is available in Appendix A.11) and found that the reason is due to unguided copy-paste's oversampling of novel classes that are being adapted, and forgetting of classes learned in the previous incremental learning stage and not in the memory-replay buffer.

Here we consider a more challenging task setting, which we term _single-instance incremental learning_. Namely, for training images provided in incremental learning stages, if there are multiple instances of a novel class in the image, we assume that only _one instance_ will be annotated. To simulate this setting, we use the instance-level segmentation annotation provided by the COCO dataset to enforce only annotation of one novel instance in every image is available to the model.

We can observe that the pseudo-labeling baseline, \(\text{PIFS}(\mathcal{L}_{2})^{\dagger}\), yields substantially worse performance when the model receives single-instance annotations. We reason this is due to noisy labels generated by the pseudo-labeling process, where novel instances are incorrectly labeled as background.

This highlights the potential of copy-paste synthesis as an alternative to the existing pseudo-labeling paradigm to cope with the more realistic single-instance setting.

In this paper, we demonstrate how judicious use of copy-paste dramatically boosts the performance of incremental segmentation methods under the few-shot setting and enables learning with partially-annotated images.

We experimented with all of these variants and found that the diversity-aware construction yields the best result in all metrics. Compared to randomly selected samples or easy samples, using hard samples result in better performance on novel categories. On the other hand, we found that using a mixture of easiest samples and hardest sample is also a good strategy, which achieves superior performance in base categories compared to other baselines, and comparable performance to our diversity-aware sampling. It is also worth noting that both the 'GreedyClass' method and the 'BalancedRegion' method achieve almost comparable results to our methods, but still slightly underperform our diversity guidance. We investigate how different probabilistic strategies to apply copy-and-paste augmentation affects GAPS.

For every selected base image, the _Aways Paste_ method randomly picks a novel category and always synthesizes an instance of the selected novel category onto the given image. Finally, the _50-50-Paste_ method is an extension of the naive copy-paste method to leverage past novel samples like vRFS.

In the result, we can observe that our method (vRFS) achieves the best result in terms of the novel IoU and harmonic mean of base and novel IoU. On the other hand, the 50-50-Paste baseline and the CAS baseline achieves better results possibly because they undersample novel classes.

We observe a steady increase in both the base IoU and novel IoU in this challenging task setting on methods augmented with GAPS.

We can observe that storing samples observed during the incremental learning stage leads to only moderate increase in novel IoU. Our PIFS(\(\mathcal{L}_{2}\))+GAPS significantly outperforms both baselines.

Fig. 5 shows qualitative results of PIFS(\(\mathcal{L}_{2}\)), PIFS(\(\mathcal{L}_{2}\))+NPS (naive copy-paste), and PIFS(\(\mathcal{L}_{2}\))+GAPS on the PASCAL-5-1 split dataset under the 1-shot setting using images on the left column. We can see that PIFS and PIFS+NPS gives reasonably good predictions on classes that are adapted recently (e.g., the cow class in the last row). However, they tend to exhibit catastrophic forgetting for classes learned earlier (e.g., the bus example in PIFS). On the other hand, the model trained using GAPS not only yields good result on recently learned classes, but also maintains promising performance on classes adapted earlier.

Input: Table 2: Ablation study of components in GAPS on PIFS(L2) on the COCO-20i dataset under 5- shot setting. Note that when only combined with the memory-replay buffer, the base IoU is higher because model has access to additional full annotations. When diversity guidance (D-guide) is disabled, D̂0 consists of random examples from the base dataset, resulting in worse base performance. When context guidance (C-guide) is disabled, a base image is uniformly sampled. When frequency guidance (F-guide) is disabled, a novel instance is sampled uniformly and is always pasted onto

Input: Figure 3: Long-term continual learning performance of PIFS(L2) and PIFS(L2)+GAPS on the COCO-20-1 split under 5-shot setting. Best view in color.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Let me evaluate the claim: "Despite the great success of copy-paste synthesis in the conventional offline visual recognition, we demonstrate substantially degraded performance of its naive extension in our online scenario, due to newly encountered challenges."

The evidence strongly supports this claim through:
1. Table 2 shows that naive copy-paste (without guidance) performs worse than the full GAPS system
2. The paper demonstrates specific challenges in the online scenario that required addressing through guidance mechanisms (frequency, diversity, and context guidance)
3. Figure 3 illustrates the performance degradation in the naive approach compared to the guided version
4. The evidence clearly shows the limitations of directly applying conventional copy-paste methods to the online scenario

The claim is well-stated and accurately reflects the empirical evidence without exaggeration. It acknowledges both the prior success and current limitations while being supported by concrete experimental results.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Despite the great success of copy-paste synthesis in the conventional _offline_ visual recognition, we demonstrate substantially degraded performance of its naive extension in our _online_ scenario, due to newly encountered challenges.

The review comment to be evaluated is:

Strength:
 (1) This paper introduces copy-paste as a synthesis technique to few-shot incremental segmentation.
 (2) Experiments show clear improvement on performance with the proposed method.

Weaknesses:
(1)  My main concern is the novelty of the paper, copy-paste technique is more about a data augmentation methods, which has already been shown effective in many segmentation task, it is not novel enough to be accepted as an ICLR paper.
(2) The paper is not clear at least for me, especially figure 1 and figure, it's really hard for readers to understand the task, although the idea is clear.

The evidence to be evaluated is:It is known that many learning-based models suffer from catastrophic forgetting (McCloskey and Cohen, 1989), a pheonomenon that causes models to perform significantly worse on old tasks when they are fine-tuned to adapt to new tasks. _Incremental learning_ studies how to enable models to adapt to new classes while mitigating catastrophic forgetting without accessing the old dataset or full-scale re-training. This problem has been studied extensively in image classification (Li and Hoiem, 2017; Lopez-Paz and Ranzato, 2017; Yoon et al., 2018; Rebuffi et al., 2017; Mallya et al., 2018; Castro et al., 2018; Bang et al., 2021); whilst relatively less work have been done to study incremental learning under the task setting of semantic segmentation (Cermelli et al., 2020; Michieli and Zanuttigh, 2019; Cha et al., 2021; Douillard et al., 2021; Zhang et al., 2022).

However, existing work relies on rich annotations and tends to fail when only a limited amount of data is available. In contrast, our work enables incremental segmentation learning with _few data_ via a guided copy-paste process, which demonstrates promising performance under the few-shot and more impoverished single-instance setting.

In comparison, our proposed method works in a more challenging and realistic setting where both base classes and novel classes need to be segmented.

While there are many works in few-shot incremental image classification (Tao et al., 2020; Cheraghian et al., 2021), relatively fewer works have been done to investigate few-shot incremental segmentation (Tian et al., 2020; Cermelli et al., 2021; Ganea et al., 2021).

Unlike Tian et al. (2020a), which only performs a single update of weights in the classifier, PIFS (Cermelli et al., 2021) apply regularization techniques to allow fine-tuning of the entire network, achieving state-of-the-art result in few-shot incremental semantic segmentation. However, PIFS (Cermelli et al., 2021) is fine-tuned on only a small number of samples, which leads to sub-optimal performance due to overfitting.

Many works (Dwibedi et al., 2017; Ghiasi et al., 2021; Dvornik et al., 2018) have been done to investigate how copy-and-paste augmentation can help with various visual tasks. Dvornik _et al._ apply copy-paste augmentation in object detection by designing a neural network to consider context and guide copy-paste. However, the context guidance method proposed by Dvornik _et al._ can not be trivially applied to our application since it requires abundant fully-annotated training data. More recently, Ghiasi _et al._ conduct extensive experiments to demonstrate the effectiveness of simple copy-paste in the instance segmentation problem. We extend the augmentation strategy from Ghiasi et al. (2021) and construct an intuitive baseline called Naive copy-Paste Synthesis (NPS) to adapt it to our online task setting. However, as we will demonstrate, such naive adaptation gives unsatisfactory performance in our task setting because of _gaps_ between the static offline learning and continual online learning. In our work, we propose a series of techniques to guide the copy-paste synthesizer to address these gaps, whose effectiveness is evident from the significant improvement from NPS.

It is a _generic and model-agnostic_ data synthesis framework that generates fully-labeled scenes from partially-annotated images of novel objects as a preprocessor to the underlying segmentation model.

During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\). The strategy by which we choose base exemplars and novel segments is _context-guided_ (Section 3.3) and _class-frequency-guided_ (Section 3.4).

We hypothesize that synthesizing novel objects onto contextually consistent base images would result in an improved learning process.

We design a context-guided sampling algorithm to select images from \(\hat{D_{0}}\) that are contextually similar to the provided partially-labeled images. GAPS uses a scene embedding network \(h:\mathcal{X}\rightarrow\mathbb{R}^{m}\) to estimate pairwise contextual similarity between any two given images. The embedding \(h\) maps an input image to a scene embedding vector in a metric space where pairwise similarity comparison between two embeddings are possible. In our system, we use an off-the-shelf VGGNet (Simonyan and Zisserman, 2015) and replace its last fully connected layer with a cosine-similarity-based classifier. The network \(h\) is trained using the Places365 (Zhou et al., 2017) dataset, which contains \(365\) scene categories and roughly 1.6 million images in its training split. During training, the scene prediction is done by comparing the predicted embedding with existing prototypes.

As one extreme, one can follow Ghiasi et al. (2021) and always apply copy-paste augmentation to paste novel objects onto every base image. However, this will lead to oversampling of novel categories in the current task, which we found to hurt the performance of existing classes. On the other hand, rarely pasting novel instances would lead to undersampling of the novel class. Therefore, to guide copy-paste in the online setting, we design a synthesis strategy called vRFS based on RFS (Repeat Factor Sampling) described by Gupta et al. (2019) to perform synthesis.

If \(c\in\mathcal{C}_{j}\) with \(j\geq 1\), we first compute its class frequency by \(f_{c}=nShot/|\hat{D}_{0}|\), where \(nShot\) denotes the number of images in \(D_{j}\) with at least one pixel of \(c\). Then, the category-wise sampling factor for \(c\) is given by \(r_{c}=\textsc{Max}(1,\sqrt{t/f_{c}})\). Note that in (Gupta et al., 2019), \(t\) is chosen as a hyperparameter to be tuned. However, we empirically found that setting \(t\) to be the multiplicative inverse of total number of classes, or \(t=1/|\cup_{1,...,j}Y_{j}|\), is enough to yield stable results across different datasets and under different few-shot settings. This eliminates the need to search a hyperparameter for different settings and make our proposed method more robust towards different task settings. During the synthesis process, we first randomly select a novel class \(c_{N}\) from \(\mathcal{C}_{j}\), and another class \(c_{o}\) from \(\cup_{1,...,j}C_{j}\setminus\{c_{N}\}\).

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories. In Table 2, we ablate guidance designs in GAPS to illustrate how different types of guidance contribute to the final incremental learning performance than naive copy-paste synthesis.

We take a closer look at step-wise performance (whose visualization is available in Appendix A.11) and found that the reason is due to unguided copy-paste's oversampling of novel classes that are being adapted, and forgetting of classes learned in the previous incremental learning stage and not in the memory-replay buffer.

Here we consider a more challenging task setting, which we term _single-instance incremental learning_. Namely, for training images provided in incremental learning stages, if there are multiple instances of a novel class in the image, we assume that only _one instance_ will be annotated. To simulate this setting, we use the instance-level segmentation annotation provided by the COCO dataset to enforce only annotation of one novel instance in every image is available to the model.

We can observe that the pseudo-labeling baseline, \(\text{PIFS}(\mathcal{L}_{2})^{\dagger}\), yields substantially worse performance when the model receives single-instance annotations. We reason this is due to noisy labels generated by the pseudo-labeling process, where novel instances are incorrectly labeled as background.

This highlights the potential of copy-paste synthesis as an alternative to the existing pseudo-labeling paradigm to cope with the more realistic single-instance setting.

In this paper, we demonstrate how judicious use of copy-paste dramatically boosts the performance of incremental segmentation methods under the few-shot setting and enables learning with partially-annotated images.

We experimented with all of these variants and found that the diversity-aware construction yields the best result in all metrics. Compared to randomly selected samples or easy samples, using hard samples result in better performance on novel categories. On the other hand, we found that using a mixture of easiest samples and hardest sample is also a good strategy, which achieves superior performance in base categories compared to other baselines, and comparable performance to our diversity-aware sampling. It is also worth noting that both the 'GreedyClass' method and the 'BalancedRegion' method achieve almost comparable results to our methods, but still slightly underperform our diversity guidance. We investigate how different probabilistic strategies to apply copy-and-paste augmentation affects GAPS.

For every selected base image, the _Aways Paste_ method randomly picks a novel category and always synthesizes an instance of the selected novel category onto the given image. Finally, the _50-50-Paste_ method is an extension of the naive copy-paste method to leverage past novel samples like vRFS.

In the result, we can observe that our method (vRFS) achieves the best result in terms of the novel IoU and harmonic mean of base and novel IoU. On the other hand, the 50-50-Paste baseline and the CAS baseline achieves better results possibly because they undersample novel classes.

We observe a steady increase in both the base IoU and novel IoU in this challenging task setting on methods augmented with GAPS.

We can observe that storing samples observed during the incremental learning stage leads to only moderate increase in novel IoU. Our PIFS(\(\mathcal{L}_{2}\))+GAPS significantly outperforms both baselines.

Fig. 5 shows qualitative results of PIFS(\(\mathcal{L}_{2}\)), PIFS(\(\mathcal{L}_{2}\))+NPS (naive copy-paste), and PIFS(\(\mathcal{L}_{2}\))+GAPS on the PASCAL-5-1 split dataset under the 1-shot setting using images on the left column. We can see that PIFS and PIFS+NPS gives reasonably good predictions on classes that are adapted recently (e.g., the cow class in the last row). However, they tend to exhibit catastrophic forgetting for classes learned earlier (e.g., the bus example in PIFS). On the other hand, the model trained using GAPS not only yields good result on recently learned classes, but also maintains promising performance on classes adapted earlier.

Input: Table 2: Ablation study of components in GAPS on PIFS(L2) on the COCO-20i dataset under 5- shot setting. Note that when only combined with the memory-replay buffer, the base IoU is higher because model has access to additional full annotations. When diversity guidance (D-guide) is disabled, D̂0 consists of random examples from the base dataset, resulting in worse base performance. When context guidance (C-guide) is disabled, a base image is uniformly sampled. When frequency guidance (F-guide) is disabled, a novel instance is sampled uniformly and is always pasted onto

Input: Figure 3: Long-term continual learning performance of PIFS(L2) and PIFS(L2)+GAPS on the COCO-20-1 split under 5-shot setting. Best view in color.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Let me evaluate the claim "Despite the great success of copy-paste synthesis in the conventional offline visual recognition, we demonstrate substantially degraded performance of its naive extension in our online scenario, due to newly encountered challenges."

This claim appears well-stated (score 1) based on several pieces of evidence:

1. Table 2 clearly shows that naive copy-paste without guidance components performs worse than the full GAPS system (25.8% vs 19.8% Novel IoU)

2. The evidence specifically states "naive adaptation gives unsatisfactory performance in our task setting because of gaps between the static offline learning and continual online learning"

3. Figure 3 demonstrates the performance degradation in the naive approach compared to the guided approach over multiple tasks

4. The review comments acknowledge the clear improvement in performance with the proposed method over naive copy-paste

The claim accurately reflects the evidence without exaggeration, pointing out real limitations of naive copy-paste in this new context while being supported by quantitative results.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Despite the great success of copy-paste synthesis in the conventional _offline_ visual recognition, we demonstrate substantially degraded performance of its naive extension in our _online_ scenario, due to newly encountered challenges.

The review comment to be evaluated is:

Strengths:
- The paper introduces the copy-paste data augmentation strategy to the few-shot incremental semantic segmentation, which seems to be a novel combination.
- The proposed guided sample selection method for memory construction and data synthesis improves the performance of several baseline methods.
- The paper is most clearly written and easy to follow.

Weaknesses:
- While the proposed data augmentation strategy helps few-shot learning, its technical novelty in the incremental semantic segmentation (ISS) is rather limited, as it is an add-on module and has to rely on the base ISS methods.
- The construction of the memory-replay buffer has several limitations and will affect the quality of the data augmentation in ISS:
1) Only the base stage has fully-annotated images that can be used for the target images in the copy-paste operation, and all the images from the incremental stages are partially labeled, which are unusable for that purpose. As a result, when the incremental learning evolves, the memory has to add samples of new classes and remove some base-stage images due to the capacity limit. Eventually, the memory buffer would be less useful.
2) The diversity-guided selection procedure in Sec 3.2 considers each class separately in sampling. However, for semantic segmentation, it is common that each image has regions of multiple classes. It is unclear how such a method can balance different classes. This is also a problem for the class-frequency-guided selection, as each image can have multiple classes. Another issue is that this method did not take into account the region sizes as it only considers the image-level sampling factor.
- There are several concerns on the experimental evaluation, which make the conclusion less convincing:
1) Both benchmarks are mainly used in the few-shot segmentation literature, which are a bit small in terms of data set size and number of classes. Similar to SSUL and other ISS works, the method should be evaluated on more diverse ISS task settings and larger dataset, such as ADE.
2) For the few-shot side, while this work adopts a different evaluation protocol (as described in Sec. 4.2), it would be more informative if it also uses the protocol in Cermelli et al 2021 for a fair comparison. Moreover, unlike the standard ISS, the training process of the few-shot setting typically has a large variance due to different training samples annotated. Thus it should report both the mean and standard deviation in the final results.
3) The gaps in the ablation study are relatively small. It would more convincing if the standard deviation can be provided. It is unclear how significant such improvements are in the few-shot learning setting.

The evidence to be evaluated is:It is known that many learning-based models suffer from catastrophic forgetting (McCloskey and Cohen, 1989), a pheonomenon that causes models to perform significantly worse on old tasks when they are fine-tuned to adapt to new tasks. _Incremental learning_ studies how to enable models to adapt to new classes while mitigating catastrophic forgetting without accessing the old dataset or full-scale re-training. This problem has been studied extensively in image classification (Li and Hoiem, 2017; Lopez-Paz and Ranzato, 2017; Yoon et al., 2018; Rebuffi et al., 2017; Mallya et al., 2018; Castro et al., 2018; Bang et al., 2021); whilst relatively less work have been done to study incremental learning under the task setting of semantic segmentation (Cermelli et al., 2020; Michieli and Zanuttigh, 2019; Cha et al., 2021; Douillard et al., 2021; Zhang et al., 2022).

However, existing work relies on rich annotations and tends to fail when only a limited amount of data is available. In contrast, our work enables incremental segmentation learning with _few data_ via a guided copy-paste process, which demonstrates promising performance under the few-shot and more impoverished single-instance setting.

In comparison, our proposed method works in a more challenging and realistic setting where both base classes and novel classes need to be segmented.

While there are many works in few-shot incremental image classification (Tao et al., 2020; Cheraghian et al., 2021), relatively fewer works have been done to investigate few-shot incremental segmentation (Tian et al., 2020; Cermelli et al., 2021; Ganea et al., 2021).

Unlike Tian et al. (2020a), which only performs a single update of weights in the classifier, PIFS (Cermelli et al., 2021) apply regularization techniques to allow fine-tuning of the entire network, achieving state-of-the-art result in few-shot incremental semantic segmentation. However, PIFS (Cermelli et al., 2021) is fine-tuned on only a small number of samples, which leads to sub-optimal performance due to overfitting.

Many works (Dwibedi et al., 2017; Ghiasi et al., 2021; Dvornik et al., 2018) have been done to investigate how copy-and-paste augmentation can help with various visual tasks. Dvornik _et al._ apply copy-paste augmentation in object detection by designing a neural network to consider context and guide copy-paste. However, the context guidance method proposed by Dvornik _et al._ can not be trivially applied to our application since it requires abundant fully-annotated training data. More recently, Ghiasi _et al._ conduct extensive experiments to demonstrate the effectiveness of simple copy-paste in the instance segmentation problem. We extend the augmentation strategy from Ghiasi et al. (2021) and construct an intuitive baseline called Naive copy-Paste Synthesis (NPS) to adapt it to our online task setting. However, as we will demonstrate, such naive adaptation gives unsatisfactory performance in our task setting because of _gaps_ between the static offline learning and continual online learning. In our work, we propose a series of techniques to guide the copy-paste synthesizer to address these gaps, whose effectiveness is evident from the significant improvement from NPS.

It is a _generic and model-agnostic_ data synthesis framework that generates fully-labeled scenes from partially-annotated images of novel objects as a preprocessor to the underlying segmentation model.

During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\). The strategy by which we choose base exemplars and novel segments is _context-guided_ (Section 3.3) and _class-frequency-guided_ (Section 3.4).

We hypothesize that synthesizing novel objects onto contextually consistent base images would result in an improved learning process.

We design a context-guided sampling algorithm to select images from \(\hat{D_{0}}\) that are contextually similar to the provided partially-labeled images. GAPS uses a scene embedding network \(h:\mathcal{X}\rightarrow\mathbb{R}^{m}\) to estimate pairwise contextual similarity between any two given images. The embedding \(h\) maps an input image to a scene embedding vector in a metric space where pairwise similarity comparison between two embeddings are possible. In our system, we use an off-the-shelf VGGNet (Simonyan and Zisserman, 2015) and replace its last fully connected layer with a cosine-similarity-based classifier. The network \(h\) is trained using the Places365 (Zhou et al., 2017) dataset, which contains \(365\) scene categories and roughly 1.6 million images in its training split. During training, the scene prediction is done by comparing the predicted embedding with existing prototypes.

As one extreme, one can follow Ghiasi et al. (2021) and always apply copy-paste augmentation to paste novel objects onto every base image. However, this will lead to oversampling of novel categories in the current task, which we found to hurt the performance of existing classes. On the other hand, rarely pasting novel instances would lead to undersampling of the novel class. Therefore, to guide copy-paste in the online setting, we design a synthesis strategy called vRFS based on RFS (Repeat Factor Sampling) described by Gupta et al. (2019) to perform synthesis.

If \(c\in\mathcal{C}_{j}\) with \(j\geq 1\), we first compute its class frequency by \(f_{c}=nShot/|\hat{D}_{0}|\), where \(nShot\) denotes the number of images in \(D_{j}\) with at least one pixel of \(c\). Then, the category-wise sampling factor for \(c\) is given by \(r_{c}=\textsc{Max}(1,\sqrt{t/f_{c}})\). Note that in (Gupta et al., 2019), \(t\) is chosen as a hyperparameter to be tuned. However, we empirically found that setting \(t\) to be the multiplicative inverse of total number of classes, or \(t=1/|\cup_{1,...,j}Y_{j}|\), is enough to yield stable results across different datasets and under different few-shot settings. This eliminates the need to search a hyperparameter for different settings and make our proposed method more robust towards different task settings. During the synthesis process, we first randomly select a novel class \(c_{N}\) from \(\mathcal{C}_{j}\), and another class \(c_{o}\) from \(\cup_{1,...,j}C_{j}\setminus\{c_{N}\}\).

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories. In Table 2, we ablate guidance designs in GAPS to illustrate how different types of guidance contribute to the final incremental learning performance than naive copy-paste synthesis.

We take a closer look at step-wise performance (whose visualization is available in Appendix A.11) and found that the reason is due to unguided copy-paste's oversampling of novel classes that are being adapted, and forgetting of classes learned in the previous incremental learning stage and not in the memory-replay buffer.

Here we consider a more challenging task setting, which we term _single-instance incremental learning_. Namely, for training images provided in incremental learning stages, if there are multiple instances of a novel class in the image, we assume that only _one instance_ will be annotated. To simulate this setting, we use the instance-level segmentation annotation provided by the COCO dataset to enforce only annotation of one novel instance in every image is available to the model.

We can observe that the pseudo-labeling baseline, \(\text{PIFS}(\mathcal{L}_{2})^{\dagger}\), yields substantially worse performance when the model receives single-instance annotations. We reason this is due to noisy labels generated by the pseudo-labeling process, where novel instances are incorrectly labeled as background.

This highlights the potential of copy-paste synthesis as an alternative to the existing pseudo-labeling paradigm to cope with the more realistic single-instance setting.

In this paper, we demonstrate how judicious use of copy-paste dramatically boosts the performance of incremental segmentation methods under the few-shot setting and enables learning with partially-annotated images.

We experimented with all of these variants and found that the diversity-aware construction yields the best result in all metrics. Compared to randomly selected samples or easy samples, using hard samples result in better performance on novel categories. On the other hand, we found that using a mixture of easiest samples and hardest sample is also a good strategy, which achieves superior performance in base categories compared to other baselines, and comparable performance to our diversity-aware sampling. It is also worth noting that both the 'GreedyClass' method and the 'BalancedRegion' method achieve almost comparable results to our methods, but still slightly underperform our diversity guidance. We investigate how different probabilistic strategies to apply copy-and-paste augmentation affects GAPS.

For every selected base image, the _Aways Paste_ method randomly picks a novel category and always synthesizes an instance of the selected novel category onto the given image. Finally, the _50-50-Paste_ method is an extension of the naive copy-paste method to leverage past novel samples like vRFS.

In the result, we can observe that our method (vRFS) achieves the best result in terms of the novel IoU and harmonic mean of base and novel IoU. On the other hand, the 50-50-Paste baseline and the CAS baseline achieves better results possibly because they undersample novel classes.

We observe a steady increase in both the base IoU and novel IoU in this challenging task setting on methods augmented with GAPS.

We can observe that storing samples observed during the incremental learning stage leads to only moderate increase in novel IoU. Our PIFS(\(\mathcal{L}_{2}\))+GAPS significantly outperforms both baselines.

Fig. 5 shows qualitative results of PIFS(\(\mathcal{L}_{2}\)), PIFS(\(\mathcal{L}_{2}\))+NPS (naive copy-paste), and PIFS(\(\mathcal{L}_{2}\))+GAPS on the PASCAL-5-1 split dataset under the 1-shot setting using images on the left column. We can see that PIFS and PIFS+NPS gives reasonably good predictions on classes that are adapted recently (e.g., the cow class in the last row). However, they tend to exhibit catastrophic forgetting for classes learned earlier (e.g., the bus example in PIFS). On the other hand, the model trained using GAPS not only yields good result on recently learned classes, but also maintains promising performance on classes adapted earlier.

Input: Table 2: Ablation study of components in GAPS on PIFS(L2) on the COCO-20i dataset under 5- shot setting. Note that when only combined with the memory-replay buffer, the base IoU is higher because model has access to additional full annotations. When diversity guidance (D-guide) is disabled, D̂0 consists of random examples from the base dataset, resulting in worse base performance. When context guidance (C-guide) is disabled, a base image is uniformly sampled. When frequency guidance (F-guide) is disabled, a novel instance is sampled uniformly and is always pasted onto

Input: Figure 3: Long-term continual learning performance of PIFS(L2) and PIFS(L2)+GAPS on the COCO-20-1 split under 5-shot setting. Best view in color.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim that "naive copy-paste extension shows substantially degraded performance in online scenarios" appears to be somewhat overstated. While the evidence does show that naive copy-paste (NPS) performs worse than GAPS, the degradation isn't necessarily "substantial." Table 2 shows incremental improvements from adding different components, and Figure 3 demonstrates better performance with GAPS vs baseline PIFS, but the differences, while meaningful, don't support characterizing the naive approach as having "substantially degraded" performance. The review comments also note that "gaps in the ablation study are relatively small."

<score>4</score>

Justification:
1. The evidence shows NPS underperforms GAPS but doesn't demonstrate "substantial" degradation
2. Table 2 shows incremental rather than dramatic differences between approaches
3. The review specifically calls out that performance gaps are "relatively small"
4. While the naive approach has limitations, characterizing it as "substantially degraded" overstates the evidence
5. More measured language about performance differences would better reflect the actual results shown


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

To address the gaps between copy-paste under the offline setting as an augmentation technique and under the online setting as a synthesis technique, we design a guided copy-paste process that improves the distribution of synthesized images by enforcing diversity of the memory-replay buffer, exploiting contextual information, and balancing class frequencies.

The evidence to be evaluated is:In contrast, our work enables incremental segmentation learning with _few data_ via a guided copy-paste process, which demonstrates promising performance under the few-shot and more impoverished single-instance setting. Furthermore, GAPS is a _model-agnostic data pre-processor_, which is orthogonal to incremental learning techniques such as regularization (Li and Hoiem, 2017).

We extend the augmentation strategy from Ghiasi et al. (2021) and construct an intuitive baseline called Naive copy-Paste Synthesis (NPS) to adapt it to our online task setting. However, as we will demonstrate, such naive adaptation gives unsatisfactory performance in our task setting because of _gaps_ between the static offline learning and continual online learning. In our work, we propose a series of techniques to guide the copy-paste synthesizer to address these gaps, whose effectiveness is evident from the significant improvement from NPS.

It is a _generic and model-agnostic_ data synthesis framework that generates fully-labeled scenes from partially-annotated images of novel objects as a preprocessor to the underlying segmentation model. After the standard base learning stage with base dataset \(D_{0}\), we build a memory-replay buffer \(\hat{D}_{0}\) using an _diversity-guided exemplar selection strategy_ (Section 3.2). During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\). The strategy by which we choose base exemplars and novel segments is _context-guided_ (Section 3.3) and _class-frequency-guided_ (Section 3.4).

We found experimentally that when a diverse data source is used (i.e., our proposed GAPS), \(\mathcal{L}_{2}\) regularization works better. To be more precise, we construct a penalization term \(\mathcal{L}_{\mathrm{REG}}\) to regularize the output before the classifier.

\[\mathcal{L}_{\mathrm{REG}}=||f_{j}(x)-f_{j-1}(x)||_{2}. \tag{1}\]

\[\mathcal{L}(x,y)=\mathcal{L}_{\mathrm{CE}}(\phi_{j}(x),y)+\lambda\mathcal{L}_{ \mathrm{REG}}, \tag{2}\]

For methods with memory-replaying (e.g., SSUL (Cha et al., 2021)), GAPS can work directly on top of their constructed buffers with minimal modification. For other methods such as PIFS (Cermelli et al., 2021), we propose a diversity-guided exemplar selection process that builds a small yet diverse memory-replay buffer \(\hat{D_{0}}\) from \(\mathcal{D}_{0}\) to mitigate catastrophic forgetting. Selecting diverse examples that are representative of the base dataset helps mitigate catastrophic forgetting, as suggested by Rebuffi et al. (2017). Inspired by Bang et al. (2021), we select samples distributed uniformly along a spectrum from easy to hard for diversity.

Intuitively, we approximate the difficulty of every sample by their similarity between the estimated prototype with learned prototypes. Estimated prototypes that are close to the learned prototype are considered easy samples and vice versa. After building a list of base samples sorted by difficulties, we select samples from equally-spaced intervals to ensure samples of all difficulties are selected for diversity.

number of exemplars \(n\) \(k\leftarrow\textsc{Floor}(n/|Y_{0}|)\)// Sample per class for\(c\) from \(1\) to \(|Y_{0}|\)do \(S_{c}\leftarrow\{(x_{i},y_{i})\in D_{0},c\in y_{i}\}\) for\((x_{i},y_{i})\in S_{c}\)do \(p_{i}\leftarrow\textsc{MAP}(x_{i},y_{i},c)\)// Pred. Proto. \(s_{i}\leftarrow\textsc{CosineSimilarity}(p_{ic},w_{c})\) endfor  Sort \(S_{c}\) by similarity score \(s_{i}\) \(ES_{c}\leftarrow\{\}\)// final exemplar set of class \(c\) for\(j=1,2,\dots,k\)do \(L_{idx}\gets j\cdot|S_{c}|/k\) \(U_{idx}\leftarrow\textsc{Min}(Lower+|S_{c}|/k,|S_{c}|)\) \((x,y)\leftarrow\textsc{Sample}(S_{c}[L_{idx}:U_{idx}])\) \(ES_{c}\gets ES_{c}\cup(x,y)\) endfor endfor \(D_{0}\leftarrow\textsc{UniformSample}(\bigcup_{i=1,\dots,|Y_{0}|}ES_{i},n)\)

We hypothesize that synthesizing novel objects onto contextually consistent base images would result in an improved learning process. For example, a TV in an apartment should more likely be pasted onto an image of another apartment rather than an outdoor landscape. We design a context-guided sampling algorithm to select images from \(\hat{D_{0}}\) that are contextually similar to the provided partially-labeled images. GAPS uses a scene embedding network \(h:\mathcal{X}\rightarrow\mathbb{R}^{m}\) to estimate pairwise contextual similarity between any two given images. The embedding \(h\) maps an input image to a scene embedding vector in a metric space where pairwise similarity comparison between two embeddings are possible. In our system, we use an off-the-shelf VGGNet (Simonyan and Zisserman, 2015) and replace its last fully connected layer with a cosine-similarity-based classifier. The network \(h\) is trained using the Places365 (Zhou et al., 2017) dataset, which contains \(365\) scene categories and roughly 1.6 million images in its training split. During training, the scene prediction is done by comparing the predicted embedding with existing prototypes.

To find contextually similar base images to each novel image, we evaluate the cosine similarity of the novel image to each of the examples in \(\hat{D}_{0}\), and constuct a contextually similar subset \(\mathbb{S}\) with \(|\hat{D}_{0}|/10\) most contextually similar examples. When there are multiple novel images, we take a union of selected examples. To allow other base scenes to be sampled to mitigate catastrophic forgetting, we sample from \(\mathbb{S}\) with a probability of \(\alpha\), and sample from \(\hat{D}_{0}\) with a probability of \(1-\alpha\), where \(\alpha\) is a hyperparameter set to \(0.9\) in our implementation.

Now the final question is, given a fully-annotated image \(x_{B}\) and an image of a novel object \(x_{N}\), how frequent should we apply copy-paste? There is a trade-off between oversampling and undersampling. As one extreme, one can follow Ghiasi et al. (2021) and always apply copy-paste augmentation to paste novel objects onto every base image. However, this will lead to oversampling of novel categories in the current task, which we found to hurt the performance of existing classes. On the other hand, rarely pasting novel instances would lead to undersampling of the novel class. Therefore, to guide copy-paste in the online setting, we design a synthesis strategy called vRFS based on RFS (Repeat Factor Sampling) described by Gupta et al. (2019) to perform synthesis. To apply vRFS, we first need to compute category-wise sampling factor \(r_{c}\) for every \(c\) as in RFS. If \(c\in\mathcal{C}_{0}\), we set \(r_{c}=1\) as since during the construction of \(\hat{D}_{0}\) we already consider class balance by class-wise uniformly sampling. If \(c\in\mathcal{C}_{j}\) with \(j\geq 1\), we first compute its class frequency by \(f_{c}=nShot/|\hat{D}_{0}|\), where \(nShot\) denotes the number of images in \(D_{j}\) with at least one pixel of \(c\). Then, the category-wise sampling factor for \(c\) is given by \(r_{c}=\textsc{Max}(1,\sqrt{t/f_{c}})\). Note that in (Gupta et al., 2019), \(t\) is chosen as a hyperparameter to be tuned. However, we empirically found that setting \(t\) to be the multiplicative inverse of total number of classes, or \(t=1/|\cup_{1,...,j}Y_{j}|\), is enough to yield stable results across different datasets and under different few-shot settings. This eliminates the need to search a hyperparameter for different settings and make our proposed method more robust towards different task settings. During the synthesis process, we first randomly select a novel class \(c_{N}\) from \(\mathcal{C}_{j}\), and another class \(c_{o}\) from \(\cup_{1,...,j}C_{j}\setminus\{c_{N}\}\). We first decide if \(c_{o}\) should be pasted onto \(x_{B}\). To apply vRFS resampling, we hallucinate two _virtual_ samples: in the first sample where copy-paste would not be applied, the image-level sampling factor is given by 1. In the second sample where copy-paste synthesis were to be performed, we would obtain a sample with image-level sampling factor of \(r_{i}=\textsc{Max}_{c\in i}r_{c}=r_{c_{o}}\). Thus, the probability to synthesize class \(c_{o}\) onto \(x_{B}\) is given by \(r_{c_{o}}/(1+r_{c_{o}})\). We then repeat the process for the novel class \(c_{N}\). Note that vRFS synthesis is applied twice for every class, resulting in up to two pasted instances of \(c_{N}\) in the final image.

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories. In Table 2, we ablate guidance designs in GAPS to illustrate how different types of guidance contribute to the final incremental learning performance than naive copy-paste synthesis.

**Our diversity-guided exemplar selection method consistently increases performance on base categories**, which suggests that it is capable of choosing diverse samples to construct a representative memory-replay buffer and mitigate catastrophic forgetting. **Context-guided sampling steadily improves performance on novel classes**, which is consistent with findings in previous work (Dvornik et al., 2018) that background context is an important factor to consider in copy-paste synthesis. **Frequency-guided probabilistic synthesis boosts results on novel classes**.

We take a closer look at step-wise performance (whose visualization is available in Appendix A.11) and found that the reason is due to unguided copy-paste's oversampling of novel classes that are being adapted, and forgetting of classes learned in the previous incremental learning stage and not in the memory-replay buffer.

In this paper, we demonstrate how judicious use of copy-paste dramatically boosts the performance of incremental segmentation methods under the few-shot setting and enables learning with partially-annotated images. Our proposed GAPS technique selects representative exemplars in the memory-replay buffer and addresses the problems of class imbalance and contextual mismatch in synthesis.

In Table 6, we study how different memory buffer construction strategies influence the performance of the model. We experiment with multiple baseline implementations to construct the memory-replay buffer:

To consider multiple classes in images, we design two baseline methods. RFS (Repeat Factor Sampling) is done by using the weighted sampling technique described in LVIS (Gupta et al., 2019), which considers multiple classes in samples by computing category-level re-sampling factor and creates image-level re-sampling factor based on the maximum category-level factor in the image. RFS assigns higher weights to rare classes and has been shown to work well in long-tailed task settings. The other baseline, 'GreedyClass', is achieved by greedily picking samples in every base class with the most number of classes. To consider the region sizes in the image, we design a base-line method, 'BalancedRegion', which uses a similar algorithm as our proposed diversity sampling method, but we replace the difficulty estimation with the number of pixels the selected class occupies in images.

We experimented with all of these variants and found that the diversity-aware construction yields the best result in all metrics. Compared to randomly selected samples or easy samples, using hard samples result in better performance on novel categories. On the other hand, we found that using a mixture of easiest samples and hardest sample is also a good strategy, which achieves superior performance in base categories compared to other baselines, and comparable performance to our diversity-aware sampling.

_CAS_ (Class-aware-sampling) is a sampling strategy from (Gupta et al., 2019) which first uniformly samples a class and then selects a sample containing instances from the selected class. In our implementation, the probability to synthesize novel objects is simply given by \(1/M\), where \(M\) is the sum of total number of base classes and seen novel classes. For every selected base image, the _Aways Paste_ method randomly picks a novel category and always synthesizes an instance of the selected novel category onto the given image. Finally, the _50-50-Paste_ method is an extension of the naive copy-paste method to leverage past novel samples like vRFS. Essentially, it replays samples from learned novel classes with 50% probability, and uses novel class from the current incremental learning task also with 50% probability.

In the result, we can observe that our method (vRFS) achieves the best result in terms of the novel IoU and harmonic mean of base and novel IoU.

using more aggresive scale jittering helps increase the diversity of samples and increases performance. Beyond confirming conclusion from previous work, such improvement alongside stronger augmentation demonstrates the effectiveness of augmentation technique even when there is few data presented, highlighting a possibility to further improve the performance by using even stronger augmentation technique.

PIFS(\(\mathcal{L}_{2}\)), augmented with GAPS, demonstrates more robustness to forgetting with many incremental learning steps than standard PIFS.

We explore an alternative memory-replay buffer implementation to add examples learned during the incremental learning stage to the memory-replay buffer, in an attempt to help performance on novel classes.

Here, 'PIFS(\(\mathcal{L}_{2}\))+MEM+PL' means that we follow SSUL and perform pseudo-labeling on partially annotated samples before adding them to memory.

Our PIFS(\(\mathcal{L}_{2}\))+GAPS significantly outperforms both baselines. We experiment with several other pretraining models as the contextual guiding model. In particular, we use ImageNet-pretrained model and pretrained image encoder from CLIP (Radford et al., 2021) to compute scene embedding for incoming images.

We conducted an additional experiment on the COCO-20-1 dataset under the 5-shot setting, where we vary the number of fully-annotated images in the memory-replay buffer to investigate the per formance of our method. As can be seen from Fig. 4, even with as few as 100 fully-annotated base images, GAPS still consistently boosts the performance of the underlying PIFS(\(\mathcal{L}_{2}\)) method.

However, they tend to exhibit catastrophic forgetting for classes learned earlier (e.g., the bus example in PIFS). On the other hand, the model trained using GAPS not only yields good result on recently learned classes, but also maintains promising performance on classes adapted earlier.

We can see that given an image of traffic lights on a railway, our scene embedding model is able to select contextually-similar scenes such as railways and street scenes for subsequent synthesis.

On the other hand, PIFS(\(\mathcal{L}_{2}\))+GAPS is able to maintain good performance on learned classes as it adapts to new classes. \begin{table}
\begin{tabular}{c|c c c} Base Set & Base & Novel & HM \\ \hline Full-set-random & \(47.9\pm 0.2\) & \(24.6\pm 0.7\) & \(32.5\) \\ Classwise-random & \(48.3\pm 0.2\) & \(24.7\pm 0.8\) & \(32.7\) \\ RFS & \(47.6\pm 0.3\) & \(25.2\pm 0.7\) & \(33.0\) \\ GreedyClass & \(48.7\pm 0.2\) & \(25.0\pm 0.7\) & \(33.0\) \\ BalancedRegion & \(48.6\pm 0.2\) & \(24.9\pm 0.8\) & \(32.9\) \\ \hline Easy & \(48.0\pm 0.2\) & \(24.2\pm 0.8\) & \(32.2\) \\ Hard & \(47.7\pm 0.2\) & \(25.2\pm 0.8\) & \(33.0\) \\ Easy-Hard-Mix & \(48.4\pm 0.3\) & \(24.8\pm 0.7\) & \(32.8\) \\ Diverse Sampling (Ours) & \(\mathbf{48.9}\pm 0.2\) & \(\mathbf{25.3}\pm 0.9\) & \(\mathbf{33.3}\) \\ \end{tabular}
\end{table}
Table 6: Ablation study of different memory-replay buffer construction strategies on COCO-20-1. We found that the performance is maximized when using our diversity-aware memory-replay buffer reconstruction strategy. Mean and 95% confidence intervals over 10 runs are reported.

Input: Table 6: Ablation study of different memory-replay buffer construction strategies on COCO-20-1. We found that the performance is maximized when using our diversity-aware memory-replay buffer reconstruction strategy. Mean and 95% confidence intervals over 10 runs are reported.

Input: Table 2: Ablation study of components in GAPS on PIFS(L2) on the COCO-20i dataset under 5- shot setting. Note that when only combined with the memory-replay buffer, the base IoU is higher because model has access to additional full annotations. When diversity guidance (D-guide) is disabled, D̂0 consists of random examples from the base dataset, resulting in worse base performance. When context guidance (C-guide) is disabled, a base image is uniformly sampled. When frequency guidance (F-guide) is disabled, a novel instance is sampled uniformly and is always pasted onto

Input: Figure 1: Our proposed method utilizes guided copy-paste augmentation to synthesize diverse training data, using as few as one single novel instance for training. For example, the model encounters an image of many motorcycles, which is novel to the model. As a result, the model incorrectly assigns learned bicycle labels to these pixels and therefore needs to be updated. Our proposed method can adapt to the novel motorcycle class with an annotation of a single motorcycle, which can be efficiently annotated; whereas previous work (Cermelli et al., 2020; 2021) require time-consuming annotation of all instances of motorcycles or even the entire image. Best view in color.

Input: Figure 6: Qualitative visualization of samples selected in the context-aware sampling process. Given an image of traffic lights in railway, our scene embedding network is able to select contextuallysimilar scenes (railway, street).

Input: Figure 2: Overview of GAPS. During the incremental learning stage, GAPS takes in as few as one annotated instance of a single image. It is more probable for GAPS to select a scene contextually similar to the provided image from memory-replay buffer D̂0. The image is then probabilistically pasted to generate synthetic fully-labeled scenes. Note that GAPS is model-agnostic, and here we use PIFS (Cermelli et al., 2021) as an example for the underlying segmentation model to illustrate how GAPS is applied as a pre-processor. Best seen in color.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that the claim is well-supported by detailed technical descriptions and experimental results. The evidence demonstrates:
- A clear diversity-guided exemplar selection strategy with quantitative improvements shown in Table 6
- Context-guided sampling using scene embeddings, validated through qualitative examples in Figure 6
- Frequency-guided synthesis using vRFS with ablation results in Table 2 showing improvements
- Each component's contribution is individually validated through ablation studies

The claim accurately describes the key technical components without exaggerating their capabilities or impact. The evidence provides thorough experimental validation for each claimed improvement.

<score>1</score>