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

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

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

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

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

The claim to be assessed is:

To address the aforementioned challenges, we propose GAPS (Guided copy-And-Paste Synthesis), which improves the training of incremental segmentation models by synthesizing fully-annotated images from partially-annotated examples.

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: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.

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).

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.

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.

Different from standard semantic segmentation, in few-shot incremental segmentation, \(\mathcal{C}\) is expanded over time through two stages. During the _base learning stage_, the model is provided with a base dataset \(\mathcal{D}_{0}=\{(x_{i},y_{i})|x_{i}\in\mathcal{X},y_{i}\in\mathcal{Y}^{ \mathcal{C}_{0}}\}\), where \(\mathcal{C}_{0}\) is a set of classes in the base dataset.

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_., \(|D_{j}|\ll|\mathcal{D}_{0}|\).

The goal of incremental learning is to optimize the model performance jointly on both previous tasks and the current task. 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 \(i\neq j\).

Fig. 2 illustrates our proposed Guided copy-Paste Synthesis (GAPS) framework for few-shot incremental segmentation. 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.

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. To apply vRFS, we first need to compute category-wise sampling factor \(r_{c}\) for every \(c\) as in RFS. If \(c\in\mathcal{C}_{0}\), we set \(r_{c}=1\) as since during the construction of \(\hat{D}_{0}\) we already consider class balance by class-wise uniformly sampling. If \(c\in\mathcal{C}_{j}\) with \(j\geq 1\), we first compute its class frequency by \(f_{c}=nShot/|\hat{D}_{0}|\), where \(nShot\) denotes the number of images in \(D_{j}\) with at least one pixel of \(c\). Then, the category-wise sampling factor for \(c\) is given by \(r_{c}=\textsc{Max}(1,\sqrt{t/f_{c}})\). Note that in (Gupta et al., 2019), \(t\) is chosen as a hyperparameter to be tuned. However, we empirically found that setting \(t\) to be the multiplicative inverse of total number of classes, or \(t=1/|\cup_{1,...,j}Y_{j}|\), is enough to yield stable results across different datasets and under different few-shot settings. This eliminates the need to search a hyperparameter for different settings and make our proposed method more robust towards different task settings. During the synthesis process, we first randomly select a novel class \(c_{N}\) from \(\mathcal{C}_{j}\), and another class \(c_{o}\) from \(\cup_{1,...,j}C_{j}\setminus\{c_{N}\}\). We first decide if \(c_{o}\) should be pasted onto \(x_{B}\). To apply vRFS resampling, we hallucinate two _virtual_ samples: in the first sample where copy-paste would not be applied, the image-level sampling factor is given by 1. In the second sample where copy-paste synthesis were to be performed, we would obtain a sample with image-level sampling factor of \(r_{i}=\textsc{Max}_{c\in i}r_{c}=r_{c_{o}}\). Thus, the probability to synthesize class \(c_{o}\) onto \(x_{B}\) is given by \(r_{c_{o}}/(1+r_{c_{o}})\). We then repeat the process for the novel class \(c_{N}\). Note that vRFS synthesis is applied twice for every class, resulting in up to two pasted instances of \(c_{N}\) in the final image.

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories.

**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**.

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.

In this paper, we demonstrate how judicious use of copy-paste dramatically boosts the performance of incremental segmentation methods under the few-shot setting and enables learning with partially-annotated images. Our proposed GAPS technique selects representative exemplars in the memory-replay buffer and addresses the problems of class imbalance and contextual mismatch in synthesis. In future work, we are interested in further application of copy-paste as a synthesis technique to cope with the background shifting problem for incremental segmentation. We believe that copy-paste can serve as a promising alternative to pseudo-labeling and modified loss to enable learning on partially-annotated images.

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.

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. This further validates the effectiveness of our approach.

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.

We can see that given an image of traffic lights on a railway, our scene embedding model is able to select contextually-similar scenes such as railways and street scenes for subsequent synthesis. Fig. 7 and Fig. 8 demonstrate how PIFS(\(\mathcal{L}_{2}\))+GAPS compare to the baseline PIFS(\(\mathcal{L}_{2}\)) across multiple step.

On the other hand, PIFS(\(\mathcal{L}_{2}\))+GAPS is able to maintain good performance on learned classes as it adapts to new classes.

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

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

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

Input: 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:
After reviewing the evidence, I find that the claim "GAPS improves the training of incremental segmentation models by synthesizing fully-annotated images from partially-annotated examples" is well-supported by extensive experimental results and detailed methodology explanation. The evidence demonstrates:

1. Clear performance improvements across multiple datasets and settings
2. Thorough technical details of how GAPS works
3. Ablation studies showing the effectiveness of each component
4. Strong quantitative and qualitative results
5. Addresses limitations and constraints appropriately

The claim is measured and accurately reflects what the evidence shows without exaggeration. The authors carefully qualify their statements and provide comprehensive support.

<score>1</score>

Justification: The claim is well-stated because:

1. It makes a specific, verifiable claim about improving training through a defined mechanism
2. The extensive experimental results directly validate the performance improvements
3. The methodology is thoroughly explained and justified
4. The authors acknowledge limitations and provide balanced analysis
5. The review comments, while noting concerns about novelty, actually support that the method works as claimed
6. The figures and tables provide clear empirical evidence supporting the stated improvements

The claim does not overstate or exaggerate beyond what is demonstrated in the evidence.


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

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

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

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

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

The claim to be assessed is:

To address the aforementioned challenges, we propose GAPS (Guided copy-And-Paste Synthesis), which improves the training of incremental segmentation models by synthesizing fully-annotated images from partially-annotated examples.

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.

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).

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.

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.

Different from standard semantic segmentation, in few-shot incremental segmentation, \(\mathcal{C}\) is expanded over time through two stages. During the _base learning stage_, the model is provided with a base dataset \(\mathcal{D}_{0}=\{(x_{i},y_{i})|x_{i}\in\mathcal{X},y_{i}\in\mathcal{Y}^{ \mathcal{C}_{0}}\}\), where \(\mathcal{C}_{0}\) is a set of classes in the base dataset.

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_., \(|D_{j}|\ll|\mathcal{D}_{0}|\).

The goal of incremental learning is to optimize the model performance jointly on both previous tasks and the current task. 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 \(i\neq j\).

Fig. 2 illustrates our proposed Guided copy-Paste Synthesis (GAPS) framework for few-shot incremental segmentation. 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.

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. To apply vRFS, we first need to compute category-wise sampling factor \(r_{c}\) for every \(c\) as in RFS. If \(c\in\mathcal{C}_{0}\), we set \(r_{c}=1\) as since during the construction of \(\hat{D}_{0}\) we already consider class balance by class-wise uniformly sampling. If \(c\in\mathcal{C}_{j}\) with \(j\geq 1\), we first compute its class frequency by \(f_{c}=nShot/|\hat{D}_{0}|\), where \(nShot\) denotes the number of images in \(D_{j}\) with at least one pixel of \(c\). Then, the category-wise sampling factor for \(c\) is given by \(r_{c}=\textsc{Max}(1,\sqrt{t/f_{c}})\). Note that in (Gupta et al., 2019), \(t\) is chosen as a hyperparameter to be tuned. However, we empirically found that setting \(t\) to be the multiplicative inverse of total number of classes, or \(t=1/|\cup_{1,...,j}Y_{j}|\), is enough to yield stable results across different datasets and under different few-shot settings. This eliminates the need to search a hyperparameter for different settings and make our proposed method more robust towards different task settings. During the synthesis process, we first randomly select a novel class \(c_{N}\) from \(\mathcal{C}_{j}\), and another class \(c_{o}\) from \(\cup_{1,...,j}C_{j}\setminus\{c_{N}\}\). We first decide if \(c_{o}\) should be pasted onto \(x_{B}\). To apply vRFS resampling, we hallucinate two _virtual_ samples: in the first sample where copy-paste would not be applied, the image-level sampling factor is given by 1. In the second sample where copy-paste synthesis were to be performed, we would obtain a sample with image-level sampling factor of \(r_{i}=\textsc{Max}_{c\in i}r_{c}=r_{c_{o}}\). Thus, the probability to synthesize class \(c_{o}\) onto \(x_{B}\) is given by \(r_{c_{o}}/(1+r_{c_{o}})\). We then repeat the process for the novel class \(c_{N}\). Note that vRFS synthesis is applied twice for every class, resulting in up to two pasted instances of \(c_{N}\) in the final image.

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories.

**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**.

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.

In this paper, we demonstrate how judicious use of copy-paste dramatically boosts the performance of incremental segmentation methods under the few-shot setting and enables learning with partially-annotated images. Our proposed GAPS technique selects representative exemplars in the memory-replay buffer and addresses the problems of class imbalance and contextual mismatch in synthesis. In future work, we are interested in further application of copy-paste as a synthesis technique to cope with the background shifting problem for incremental segmentation. We believe that copy-paste can serve as a promising alternative to pseudo-labeling and modified loss to enable learning on partially-annotated images.

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.

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. This further validates the effectiveness of our approach.

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.

We can see that given an image of traffic lights on a railway, our scene embedding model is able to select contextually-similar scenes such as railways and street scenes for subsequent synthesis. Fig. 7 and Fig. 8 demonstrate how PIFS(\(\mathcal{L}_{2}\))+GAPS compare to the baseline PIFS(\(\mathcal{L}_{2}\)) across multiple step.

On the other hand, PIFS(\(\mathcal{L}_{2}\))+GAPS is able to maintain good performance on learned classes as it adapts to new classes.

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

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

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

Input: 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:
After reviewing the evidence, the claim appears well-supported but slightly overstated. While the paper demonstrates that GAPS effectively improves incremental segmentation through guided copy-paste synthesis from partially-annotated examples, some limitations exist:

1. The reviewer notes the technical novelty is limited as it relies on base ISS methods
2. The memory-replay buffer construction has limitations that affect augmentation quality
3. The performance improvements, while consistent, show relatively small gaps in ablation studies
4. The method was not extensively evaluated on larger, more diverse datasets

However, the extensive experimental results and qualitative examples do validate the core claim about improving training through synthesis. The framework shows consistent gains across different settings and methods.

<score>2</score>

Justification: The claim is generally accurate but could be more measured in acknowledging limitations and dependencies on underlying methods. The evidence supports the fundamental innovation while revealing some constraints not fully addressed in the original claim.


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:

This paper proposes a data synthesizer, Guided copy-And-Paste Synthesis (GAPS), that improves the performance of few-shot incremental segmentation in a _model-agnostic_ fashion.

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

However, few-shot semantic segmentation methods produce novel-class-only binary foreground-background segmentation. 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). Tian et al. (2020a) designs a meta-learning-based classifier that adjusts learned prototypes by modeling interaction between base classes and incoming novel class. Unlike Tian et al. (2020a), which only performs a single update of weights in the classifier, PIFS (Cermelli et al., 2021) apply regularization techniques to allow fine-tuning of the entire network, achieving state-of-the-art result in few-shot incremental semantic segmentation. However, PIFS (Cermelli et al., 2021) is fine-tuned on only a small number of samples, which leads to sub-optimal performance due to overfitting. In addition, PIFS requires fully-annotated images as input, which hinders its potential for practical applications.

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.

Different from standard semantic segmentation, in few-shot incremental segmentation, \(\mathcal{C}\) is expanded over time through two stages.

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_., \(|D_{j}|\ll|\mathcal{D}_{0}|\).

The goal of incremental learning is to optimize the model performance jointly on both previous tasks and the current task.

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.

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.

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.

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. We follow literature in few-shot segmentation and few-shot incremental segmentation (Shaban et al., 2017; Nguyen and Todorovic, 2019; Tian et al., 2020; Cermelli et al., 2021) and evaluate our model on the PASCAL-5\({}^{\text{i}}\) dataset (Shaban et al., 2017) and the COCO-20\({}^{\text{i}}\) dataset (Nguyen and Todorovic, 2019).

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories.

In 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.

Here, _vRFS_ (virtual RFS) is our proposed method.

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

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

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. 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.

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. To allow fair comparisons with existing methods (Cermelli et al., 2021; Cha et al., 2021; Cermelli et al., 2020), we use the same Deeplab-V3 (Chen et al., 2017) architecture with ResNet-101 (He et al., 2016) backbone.

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.

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.

Fig. 7 and Fig. 8 demonstrate how PIFS(\(\mathcal{L}_{2}\))+GAPS compare to the baseline PIFS(\(\mathcal{L}_{2}\)) across multiple step.

These visualizations demonstrate how PIFS(\(\mathcal{L}_{2}\)) forgets learned classes earlier as more incremental learning steps occur. On the other hand, PIFS(\(\mathcal{L}_{2}\))+GAPS is able to maintain good performance on learned classes as it adapts to new classes.

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 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 claim and evidence, this appears to be a well-stated claim that accurately reflects what GAPS actually delivers. The evidence demonstrates that:

1. GAPS is indeed model-agnostic, working with multiple different segmentation models (PIFS, SSUL) as claimed
2. The paper shows clear performance improvements in few-shot scenarios across different datasets and settings
3. The method works as a data pre-processor that is independent of the underlying model architecture
4. Results are backed by extensive experiments and ablation studies

The claim avoids overstatement by specifically noting it "improves" performance rather than making more extreme claims about solving the problem entirely. The evidence supports all key aspects of the claim without exaggeration.

<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:

This paper proposes a data synthesizer, Guided copy-And-Paste Synthesis (GAPS), that improves the performance of few-shot incremental segmentation in a _model-agnostic_ fashion.

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: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).

However, few-shot semantic segmentation methods produce novel-class-only binary foreground-background segmentation. 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). Tian et al. (2020a) designs a meta-learning-based classifier that adjusts learned prototypes by modeling interaction between base classes and incoming novel class. Unlike Tian et al. (2020a), which only performs a single update of weights in the classifier, PIFS (Cermelli et al., 2021) apply regularization techniques to allow fine-tuning of the entire network, achieving state-of-the-art result in few-shot incremental semantic segmentation. However, PIFS (Cermelli et al., 2021) is fine-tuned on only a small number of samples, which leads to sub-optimal performance due to overfitting. In addition, PIFS requires fully-annotated images as input, which hinders its potential for practical applications.

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.

Different from standard semantic segmentation, in few-shot incremental segmentation, \(\mathcal{C}\) is expanded over time through two stages.

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_., \(|D_{j}|\ll|\mathcal{D}_{0}|\).

The goal of incremental learning is to optimize the model performance jointly on both previous tasks and the current task.

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.

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.

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.

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. We follow literature in few-shot segmentation and few-shot incremental segmentation (Shaban et al., 2017; Nguyen and Todorovic, 2019; Tian et al., 2020; Cermelli et al., 2021) and evaluate our model on the PASCAL-5\({}^{\text{i}}\) dataset (Shaban et al., 2017) and the COCO-20\({}^{\text{i}}\) dataset (Nguyen and Todorovic, 2019).

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories.

In 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.

Here, _vRFS_ (virtual RFS) is our proposed method.

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

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

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. 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.

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. To allow fair comparisons with existing methods (Cermelli et al., 2021; Cha et al., 2021; Cermelli et al., 2020), we use the same Deeplab-V3 (Chen et al., 2017) architecture with ResNet-101 (He et al., 2016) backbone.

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.

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.

Fig. 7 and Fig. 8 demonstrate how PIFS(\(\mathcal{L}_{2}\))+GAPS compare to the baseline PIFS(\(\mathcal{L}_{2}\)) across multiple step.

These visualizations demonstrate how PIFS(\(\mathcal{L}_{2}\)) forgets learned classes earlier as more incremental learning steps occur. On the other hand, PIFS(\(\mathcal{L}_{2}\))+GAPS is able to maintain good performance on learned classes as it adapts to new classes.

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 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:
After reviewing the evidence, the claim that GAPS is a model-agnostic data synthesizer that improves few-shot incremental segmentation performance appears well-stated. The evidence demonstrates:
- GAPS works with different models (PIFS, SSUL) as a pre-processor
- Consistent performance improvements across multiple datasets and settings
- Validation on partially-annotated images
- Clear explanations of methodology and mechanisms
- Comprehensive experimental results showing improvements over baselines

The review comments also support the claim's validity while noting some limitations around ablation studies and baselines. However, these don't invalidate the core claim about GAPS being model-agnostic and improving performance.

<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:

This paper proposes a data synthesizer, Guided copy-And-Paste Synthesis (GAPS), that improves the performance of few-shot incremental segmentation in a _model-agnostic_ fashion.

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: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).

However, few-shot semantic segmentation methods produce novel-class-only binary foreground-background segmentation. 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). Tian et al. (2020a) designs a meta-learning-based classifier that adjusts learned prototypes by modeling interaction between base classes and incoming novel class. Unlike Tian et al. (2020a), which only performs a single update of weights in the classifier, PIFS (Cermelli et al., 2021) apply regularization techniques to allow fine-tuning of the entire network, achieving state-of-the-art result in few-shot incremental semantic segmentation. However, PIFS (Cermelli et al., 2021) is fine-tuned on only a small number of samples, which leads to sub-optimal performance due to overfitting. In addition, PIFS requires fully-annotated images as input, which hinders its potential for practical applications.

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.

Different from standard semantic segmentation, in few-shot incremental segmentation, \(\mathcal{C}\) is expanded over time through two stages.

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_., \(|D_{j}|\ll|\mathcal{D}_{0}|\).

The goal of incremental learning is to optimize the model performance jointly on both previous tasks and the current task.

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.

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.

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.

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. We follow literature in few-shot segmentation and few-shot incremental segmentation (Shaban et al., 2017; Nguyen and Todorovic, 2019; Tian et al., 2020; Cermelli et al., 2021) and evaluate our model on the PASCAL-5\({}^{\text{i}}\) dataset (Shaban et al., 2017) and the COCO-20\({}^{\text{i}}\) dataset (Nguyen and Todorovic, 2019).

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories.

In 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.

Here, _vRFS_ (virtual RFS) is our proposed method.

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

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

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. 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.

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. To allow fair comparisons with existing methods (Cermelli et al., 2021; Cha et al., 2021; Cermelli et al., 2020), we use the same Deeplab-V3 (Chen et al., 2017) architecture with ResNet-101 (He et al., 2016) backbone.

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.

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.

Fig. 7 and Fig. 8 demonstrate how PIFS(\(\mathcal{L}_{2}\))+GAPS compare to the baseline PIFS(\(\mathcal{L}_{2}\)) across multiple step.

These visualizations demonstrate how PIFS(\(\mathcal{L}_{2}\)) forgets learned classes earlier as more incremental learning steps occur. On the other hand, PIFS(\(\mathcal{L}_{2}\))+GAPS is able to maintain good performance on learned classes as it adapts to new classes.

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 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:
After reviewing the claim and evidence, I find that the claim about GAPS being "model-agnostic" is well-supported by multiple pieces of evidence. The paper demonstrates GAPS working with different incremental learning methods (SSUL, PIFS), explicitly states it is "generic and model-agnostic," and shows implementation across various architectures and datasets. The review comments don't dispute this claim. The results shown in figures and tables demonstrate consistent performance improvements across different model architectures. The claim appears to be accurate without exaggeration.

<score>1</score>

Justification:
1. Direct evidence states GAPS is "model-agnostic" and can work with many incremental segmentation models
2. Demonstrated compatibility with multiple methods (SSUL, PIFS) with minimal modifications
3. Results show consistent improvements across different architectures
4. Claims are appropriately scoped to the evidence presented
5. Reviewer comments focus on novelty rather than disputing model-agnostic nature
6. Implementation details and experimental results substantiate the model-agnostic claim