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:

Furthermore, we demonstrate the strength of GAPS to cope with a more challenging task setting where only one instance out of many novel instances in an image is annotated, which highlights copy-paste as a better alternative to pseudo-labeling or modified loss for practical incremental segmentation applications.

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.

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).

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. In addition, PIFS requires fully-annotated images as input, which hinders its potential for practical applications.

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.

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.

During the _incremental learning stage_, a sequence of tasks \(\{D_{1},D_{2},\dots\}\) with novel categories is presented to the model, where \(D_{j}=\{(x_{i},y_{i})|x_{i}\in\mathcal{X},y_{i}\in\mathcal{Y}^{\mathcal{C}_{j}}\}\) and \(\mathcal{C}_{j}\) is a set of classes for task \(D_{j}\). In few-shot learning, the size of the training sets for the novel tasks is small, _i.e_., \(|D_{j}|\ll|\mathcal{D}_{0}|\).

To enforce the partially-annotated image setting, we follow Cermelli _et al_. and assume that only novel classes are annotated, _i.e_., \(\mathcal{C}_{i}\cap C_{j}=\varnothing\) for all \(i\neq j\).

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.

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. Here, we present an algorithm (Algorithm 1) to construct \(\hat{D_{0}}\) by exploiting the Masked Average Pooling (Map) function from Cermelli et al. (2021). 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.

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.

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 \(\cup_{1,...,j}C_{j}\setminus\{c_{N}\}\).

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.

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. Here we allow PIFS(\(\mathcal{L}_{2}\))\({}^{\dagger}\) to have access to additional information - the annotation of other non-novel background pixels

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. On the contrary, \(\text{PIFS}(\mathcal{L}_{2})\)+GAPS shows only a minor performance decrease with single instances. 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 believe that copy-paste can serve as a promising alternative to pseudo-labeling and modified loss to enable learning on partially-annotated images.

Finally, the ability to learn with as few as one annotated instance in an image raises several intriguing possibilities. For example, integrating our work with learning-based interactive segmentation will enable human operators to continually and adaptively teach novel classes and correct failed predictions.

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. 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.

We perform an additional experiment on COCO-20-1 where a long few-shot incremental learning sequence (60 base; 20 novel classes with 20 incremental steps; 5-shot) is used.

PIFS(\(\mathcal{L}_{2}\)), augmented with GAPS, demonstrates more robustness to forgetting with many incremental learning steps than standard PIFS. To validate our approach on the ADE20k dataset (Zhou et al., 2017, 2019), we ran a suite of experiments where we follow the ADE100-5 (11 steps) setup discussed in (Cha et al., 2021) and modify it as 5-shot learning. We tested PIFS(\(\mathcal{L}_{2}\)) and PIFS(\(\mathcal{L}_{2}\))+GAPS on this setting. The results are given in Table 9. We observe a steady increase in both the base IoU and novel IoU in this challenging task setting on methods augmented with GAPS.

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.

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.

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.

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 9: Performance of PIFS(L2) and PIFS(L2)+GAPS on the ADE100-5 disjoint 5-shot setting. Mean and standard deviation over 5 runs are reported.

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:
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 appears somewhat overstated given the available evidence. While GAPS does show improvements in single-instance annotation scenarios compared to pseudo-labeling baselines, several key limitations noted in the review weaken the strength of the conclusion:

1. The experiments were conducted on relatively small datasets
2. High variance in few-shot settings wasn't fully addressed with standard deviations
3. Limited evaluation on larger, more diverse datasets like ADE
4. Relatively small performance gaps in ablation studies

The evidence shows GAPS works better than pseudo-labeling in the single-instance case, but claiming it as definitively "better" for "practical applications" overreaches given these limitations. A more measured statement acknowledging these constraints would be more appropriate.

<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:

To this end, GAPS (i) addresses the partial-annotation problem by leveraging copy-paste to generate fully-labeled data for training, (ii) helps augment the few images of novel objects by introducing a guided sampling process, and (iii) mitigates catastrophic forgetting by employing a diverse memory-replay buffer.

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.

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).

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. Dvornik _et al._ apply copy-paste augmentation in object detection by designing a neural network to consider context and guide copy-paste.

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.

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).

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 \(D_{0}\leftarrow\textsc{UniformSample}(\bigcup_{i=1,\dots,|Y_{0}|}ES_{i},n)\) ```**Algorithm 1** Construct Memory-replay Buffer 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.

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.

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 \(r_{c}=\textsc{Max}(1,\sqrt{t/f_{c}})\).

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.

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.

**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. Though the semantic background shift proposed by Cermelli et al. (2020) relaxes the requirement to provide full segmentation annotations, it still requires _all novel instances_ in images to be annotated, which can be time-consuming to obtain in cluttered scenes and hinder potential applications. 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.

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 also believe that further optimizing exemplar selection and sampling strategies can lead to better guidance and lead to even better performance.

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: _Full-set-random_ denotes samples that are uniformly sampled from the base training set. _Classwise-random_ denotes randomly selecting even number of samples from each base class. 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. Intuitively, easy samples help model maintain learned prototypes during incremental learning, and hard samples help model distinguish decision boundaries. Hence, based on our sample difficulty ranking method based on prototypical distances, we also construct a few variants: _Easy_ denotes samples whose predicted embeddings are closest to learned prototypes; _Hard_ represents difficult samples whose predicted embedding are most distant to learned prototypes in the learned metric space; _Easy-Hard-Mix_ denotes equal mixtures of samples drawn from easiest samples and hardest samples. Lastly, _Diverse-Sampling_ is our proposed method, where samples of all difficulties are equally drawn. 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.

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

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.

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. We perform an additional experiment on COCO-20-1 where a long few-shot incremental learning sequence (60 base; 20 novel classes with 20 incremental steps; 5-shot) is used.

PIFS(\(\mathcal{L}_{2}\)), augmented with GAPS, demonstrates more robustness to forgetting with many incremental learning steps than standard PIFS. To validate our approach on the ADE20k dataset (Zhou et al., 2017, 2019), we ran a suite of experiments where we follow the ADE100-5 (11 steps) setup discussed in (Cha et al., 2021) and modify it as 5-shot learning. We tested PIFS(\(\mathcal{L}_{2}\)) and PIFS(\(\mathcal{L}_{2}\))+GAPS on this setting.

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

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. Since examples observed during the incremental learning stage are only partially annotated, they can not be directly added to the memory-replay buffer.

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.

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.

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.

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: Figure 4: Performance of PIFS(L2)+GAPS on the COCO-20-1 split under 5-shot setting with varying memory-replay buffer size. Best view in color.

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: Figure 5: Qualitative results on the PASCAL-5-1 split under 1-shot setting. Sequential adaptation to five classes are performed in the order indicated by the blue arrow on the left. Visualization is done after all adaptation steps are completed. Observe, particularly in rows 1, 3, and 4, that our method mitigates catastrophic forgetting and improves performance on both base and novel classes compared to PIFS and PIFS+NPS. (Best viewed 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 of the extensive evidence provided, the claim appears to be well-stated and accurately reflects what is demonstrated in the research. The evidence shows that:

1. GAPS effectively addresses partial annotation through copy-paste synthesis, demonstrated through quantitative results and ablation studies
2. The guided sampling process for novel objects is well-documented with clear methodology and empirical validation
3. The diversity-guided memory replay buffer is thoroughly explained and experimentally proven to mitigate catastrophic forgetting

The claim's three main points are each strongly supported by detailed methodology, algorithms, and experimental results showing consistent performance improvements. The authors maintain appropriate scientific caution in their statements without overstating capabilities.

<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:

To this end, GAPS (i) addresses the partial-annotation problem by leveraging copy-paste to generate fully-labeled data for training, (ii) helps augment the few images of novel objects by introducing a guided sampling process, and (iii) mitigates catastrophic forgetting by employing a diverse memory-replay buffer.

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.

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).

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. Dvornik _et al._ apply copy-paste augmentation in object detection by designing a neural network to consider context and guide copy-paste.

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.

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).

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 \(D_{0}\leftarrow\textsc{UniformSample}(\bigcup_{i=1,\dots,|Y_{0}|}ES_{i},n)\) ```**Algorithm 1** Construct Memory-replay Buffer 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.

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.

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 \(r_{c}=\textsc{Max}(1,\sqrt{t/f_{c}})\).

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.

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.

**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. Though the semantic background shift proposed by Cermelli et al. (2020) relaxes the requirement to provide full segmentation annotations, it still requires _all novel instances_ in images to be annotated, which can be time-consuming to obtain in cluttered scenes and hinder potential applications. 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.

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 also believe that further optimizing exemplar selection and sampling strategies can lead to better guidance and lead to even better performance.

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: _Full-set-random_ denotes samples that are uniformly sampled from the base training set. _Classwise-random_ denotes randomly selecting even number of samples from each base class. 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. Intuitively, easy samples help model maintain learned prototypes during incremental learning, and hard samples help model distinguish decision boundaries. Hence, based on our sample difficulty ranking method based on prototypical distances, we also construct a few variants: _Easy_ denotes samples whose predicted embeddings are closest to learned prototypes; _Hard_ represents difficult samples whose predicted embedding are most distant to learned prototypes in the learned metric space; _Easy-Hard-Mix_ denotes equal mixtures of samples drawn from easiest samples and hardest samples. Lastly, _Diverse-Sampling_ is our proposed method, where samples of all difficulties are equally drawn. 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.

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

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.

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. We perform an additional experiment on COCO-20-1 where a long few-shot incremental learning sequence (60 base; 20 novel classes with 20 incremental steps; 5-shot) is used.

PIFS(\(\mathcal{L}_{2}\)), augmented with GAPS, demonstrates more robustness to forgetting with many incremental learning steps than standard PIFS. To validate our approach on the ADE20k dataset (Zhou et al., 2017, 2019), we ran a suite of experiments where we follow the ADE100-5 (11 steps) setup discussed in (Cha et al., 2021) and modify it as 5-shot learning. We tested PIFS(\(\mathcal{L}_{2}\)) and PIFS(\(\mathcal{L}_{2}\))+GAPS on this setting.

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

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. Since examples observed during the incremental learning stage are only partially annotated, they can not be directly added to the memory-replay buffer.

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.

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.

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.

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: Figure 4: Performance of PIFS(L2)+GAPS on the COCO-20-1 split under 5-shot setting with varying memory-replay buffer size. Best view in color.

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: Figure 5: Qualitative results on the PASCAL-5-1 split under 1-shot setting. Sequential adaptation to five classes are performed in the order indicated by the blue arrow on the left. Visualization is done after all adaptation steps are completed. Observe, particularly in rows 1, 3, and 4, that our method mitigates catastrophic forgetting and improves performance on both base and novel classes compared to PIFS and PIFS+NPS. (Best viewed 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:
Summary of evaluation:
Based on the extensive evidence provided, the claim appears to be well-stated. The paper thoroughly demonstrates how GAPS addresses each component mentioned: (1) The copy-paste technique effectively generates fully-labeled data from partial annotations, (2) The guided sampling process helps with few-shot/single-instance novel objects through context-aware and frequency-guided synthesis, and (3) The diversity-guided memory buffer consistently improves base category performance and mitigates catastrophic forgetting, as shown in ablation studies. The experimental results and ablation studies provide strong empirical support for each part of the claim without exaggeration.

<score>1</score>

Justification:
The claim closely aligns with the evidence and uses measured language without overstating capabilities. Key supporting evidence includes:
- Ablation studies showing the effectiveness of diversity-guided exemplar selection for maintaining base performance
- Context-guided sampling improving novel class performance
- Frequency-guided synthesis demonstrating benefits for balancing classes
- Results showing GAPS enables learning from partial annotations and single-instance settings
- Clear experimental validation of each component's contribution

The reviewers' comments, while suggesting some areas for additional experiments, do not contradict the core claims made.


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 this end, GAPS (i) addresses the partial-annotation problem by leveraging copy-paste to generate fully-labeled data for training, (ii) helps augment the few images of novel objects by introducing a guided sampling process, and (iii) mitigates catastrophic forgetting by employing a diverse memory-replay buffer.

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.

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).

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. Dvornik _et al._ apply copy-paste augmentation in object detection by designing a neural network to consider context and guide copy-paste.

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.

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).

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 \(D_{0}\leftarrow\textsc{UniformSample}(\bigcup_{i=1,\dots,|Y_{0}|}ES_{i},n)\) ```**Algorithm 1** Construct Memory-replay Buffer 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.

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.

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 \(r_{c}=\textsc{Max}(1,\sqrt{t/f_{c}})\).

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.

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.

**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. Though the semantic background shift proposed by Cermelli et al. (2020) relaxes the requirement to provide full segmentation annotations, it still requires _all novel instances_ in images to be annotated, which can be time-consuming to obtain in cluttered scenes and hinder potential applications. 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.

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 also believe that further optimizing exemplar selection and sampling strategies can lead to better guidance and lead to even better performance.

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: _Full-set-random_ denotes samples that are uniformly sampled from the base training set. _Classwise-random_ denotes randomly selecting even number of samples from each base class. 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. Intuitively, easy samples help model maintain learned prototypes during incremental learning, and hard samples help model distinguish decision boundaries. Hence, based on our sample difficulty ranking method based on prototypical distances, we also construct a few variants: _Easy_ denotes samples whose predicted embeddings are closest to learned prototypes; _Hard_ represents difficult samples whose predicted embedding are most distant to learned prototypes in the learned metric space; _Easy-Hard-Mix_ denotes equal mixtures of samples drawn from easiest samples and hardest samples. Lastly, _Diverse-Sampling_ is our proposed method, where samples of all difficulties are equally drawn. 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.

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

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.

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. We perform an additional experiment on COCO-20-1 where a long few-shot incremental learning sequence (60 base; 20 novel classes with 20 incremental steps; 5-shot) is used.

PIFS(\(\mathcal{L}_{2}\)), augmented with GAPS, demonstrates more robustness to forgetting with many incremental learning steps than standard PIFS. To validate our approach on the ADE20k dataset (Zhou et al., 2017, 2019), we ran a suite of experiments where we follow the ADE100-5 (11 steps) setup discussed in (Cha et al., 2021) and modify it as 5-shot learning. We tested PIFS(\(\mathcal{L}_{2}\)) and PIFS(\(\mathcal{L}_{2}\))+GAPS on this setting.

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

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. Since examples observed during the incremental learning stage are only partially annotated, they can not be directly added to the memory-replay buffer.

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.

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.

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.

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: Figure 4: Performance of PIFS(L2)+GAPS on the COCO-20-1 split under 5-shot setting with varying memory-replay buffer size. Best view in color.

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: Figure 5: Qualitative results on the PASCAL-5-1 split under 1-shot setting. Sequential adaptation to five classes are performed in the order indicated by the blue arrow on the left. Visualization is done after all adaptation steps are completed. Observe, particularly in rows 1, 3, and 4, that our method mitigates catastrophic forgetting and improves performance on both base and novel classes compared to PIFS and PIFS+NPS. (Best viewed 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 the extensive evidence provided, I'll evaluate the claim about GAPS addressing (i) partial-annotation via copy-paste, (ii) few-shot novel objects via guided sampling, and (iii) catastrophic forgetting via memory-replay buffer.

The evidence strongly supports all three components with detailed methodology and experimental results. The copy-paste approach is shown to work effectively with partial annotations, the context and frequency-guided sampling improves performance on novel classes, and the diversity-guided memory buffer demonstrably helps prevent catastrophic forgetting (as shown in Table 6 and qualitative results). The claim accurately represents the capabilities and contributions without exaggeration.

<score>1</score>

The claim is well-stated, carefully describing the key technical components while avoiding hyperbole about their effectiveness. The reviewer's concern about novelty does not impact the factual accuracy of the claim itself. The experimental evidence and ablation studies thoroughly validate each claimed contribution.