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:

Our GAPS method differs from a naive (e.g., uniform random sampling) copy-paste process by a _guided_ strategy that considers diversity of the memory-replay buffer, imbalanced class frequencies between base classes and novel classes, and contextual similarity of images.

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

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

After the standard base learning stage with base dataset \(D_{0}\), we build a memory-replay buffer \(\hat{D}_{0}\) using an _diversity-guided exemplar selection strategy_ (Section 3.2). During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\). The strategy by which we choose base exemplars and novel segments is _context-guided_ (Section 3.3) and _class-frequency-guided_ (Section 3.4).

We found experimentally that when a diverse data source is used (i.e., our proposed GAPS), \(\mathcal{L}_{2}\) regularization works better.

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.

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.

Hence, the context-guided sampling algorithm poses only minor computational overhead to GAPS.

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

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 \(x_{B}\).

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.

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

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

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.

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.

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.

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. 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 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. We experiment with several other pretraining models as the contextual guiding model. In particular, we use ImageNet-pretrained model and pretrained image encoder from CLIP (Radford et al., 2021) to compute scene embedding for incoming images.

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

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.

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

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

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

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

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

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

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

Output:
After reviewing the evidence, I find that the claim about GAPS being a guided strategy considering diversity, class imbalance, and contextual similarity is well-supported by substantial experimental evidence and detailed methodology descriptions. The evidence shows:

1. Diversity-guided exemplar selection is clearly demonstrated through the difficulty-based sampling approach
2. Class frequency guidance is implemented via vRFS with documented sampling factors and probabilities
3. Contextual similarity is implemented using scene embeddings and validated through qualitative examples

The ablation studies in Table 2 quantitatively validate each component's contribution. Figure 6 provides visual proof of the contextual similarity approach working as claimed. The claim appears to accurately reflect the evidence 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:

Our GAPS method differs from a naive (e.g., uniform random sampling) copy-paste process by a _guided_ strategy that considers diversity of the memory-replay buffer, imbalanced class frequencies between base classes and novel classes, and contextual similarity of images.

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).

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.

After the standard base learning stage with base dataset \(D_{0}\), we build a memory-replay buffer \(\hat{D}_{0}\) using an _diversity-guided exemplar selection strategy_ (Section 3.2). During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\). The strategy by which we choose base exemplars and novel segments is _context-guided_ (Section 3.3) and _class-frequency-guided_ (Section 3.4).

We found experimentally that when a diverse data source is used (i.e., our proposed GAPS), \(\mathcal{L}_{2}\) regularization works better.

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.

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.

Hence, the context-guided sampling algorithm poses only minor computational overhead to GAPS.

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

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 \(x_{B}\).

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.

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

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

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.

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.

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.

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. 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 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. We experiment with several other pretraining models as the contextual guiding model. In particular, we use ImageNet-pretrained model and pretrained image encoder from CLIP (Radford et al., 2021) to compute scene embedding for incoming images.

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

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.

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

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

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

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

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

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

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

Output:
After reviewing the evidence, the claim appears to be well-stated and accurately reflects the methodology and contributions of GAPS. The evidence demonstrates that:

1. The diversity guidance is shown through their exemplar selection strategy that considers samples of varying difficulties
2. Class frequency guidance is implemented through vRFS sampling
3. Contextual similarity is addressed using scene embeddings from Places365 and demonstrated through ablation studies

The ablation studies in Table 2 specifically validate each component's contribution. The review comments raise some valid points about additional experiments that could strengthen the findings, but they don't invalidate the core claims about how GAPS differs from naive copy-paste.

<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:

Our GAPS method differs from a naive (e.g., uniform random sampling) copy-paste process by a _guided_ strategy that considers diversity of the memory-replay buffer, imbalanced class frequencies between base classes and novel classes, and contextual similarity of images.

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).

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.

After the standard base learning stage with base dataset \(D_{0}\), we build a memory-replay buffer \(\hat{D}_{0}\) using an _diversity-guided exemplar selection strategy_ (Section 3.2). During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\). The strategy by which we choose base exemplars and novel segments is _context-guided_ (Section 3.3) and _class-frequency-guided_ (Section 3.4).

We found experimentally that when a diverse data source is used (i.e., our proposed GAPS), \(\mathcal{L}_{2}\) regularization works better.

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.

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.

Hence, the context-guided sampling algorithm poses only minor computational overhead to GAPS.

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

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 \(x_{B}\).

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.

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

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

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.

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.

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.

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. 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 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. We experiment with several other pretraining models as the contextual guiding model. In particular, we use ImageNet-pretrained model and pretrained image encoder from CLIP (Radford et al., 2021) to compute scene embedding for incoming images.

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

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.

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

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

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

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

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

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

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

Output:
After reviewing the claim and evidence, I find the claim to be well-stated (score of 1). The evidence strongly supports all three key aspects mentioned in the claim:

1. Diversity consideration is demonstrated through the diversity-guided exemplar selection strategy, with ablation studies showing its effectiveness for base category performance
2. Class frequency imbalance is addressed through vRFS, with clear methodology and empirical validation showing improved novel class performance
3. Contextual similarity is implemented via scene embedding and similarity matching, with Table 2 showing its positive impact on novel class performance

The claim carefully avoids overstatement by simply stating these aspects are "considered" rather than making stronger claims about their effectiveness. The ablation studies and experimental results provide direct evidence supporting each component.

<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:

Our GAPS method differs from a naive (e.g., uniform random sampling) copy-paste process by a _guided_ strategy that considers diversity of the memory-replay buffer, imbalanced class frequencies between base classes and novel classes, and contextual similarity of images.

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

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

After the standard base learning stage with base dataset \(D_{0}\), we build a memory-replay buffer \(\hat{D}_{0}\) using an _diversity-guided exemplar selection strategy_ (Section 3.2). During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\). The strategy by which we choose base exemplars and novel segments is _context-guided_ (Section 3.3) and _class-frequency-guided_ (Section 3.4).

We found experimentally that when a diverse data source is used (i.e., our proposed GAPS), \(\mathcal{L}_{2}\) regularization works better.

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.

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.

Hence, the context-guided sampling algorithm poses only minor computational overhead to GAPS.

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

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 \(x_{B}\).

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.

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

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

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.

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.

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.

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. 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 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. We experiment with several other pretraining models as the contextual guiding model. In particular, we use ImageNet-pretrained model and pretrained image encoder from CLIP (Radford et al., 2021) to compute scene embedding for incoming images.

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

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.

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

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

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

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

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

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

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

Output:
After carefully reviewing the evidence, I find that the claim is generally accurate but somewhat overstated. While the evidence demonstrates that GAPS does employ guided strategies for diversity, class frequencies, and contextual similarity, some limitations exist:

1. The diversity-guided selection shows comparable results to simpler baselines like 'GreedyClass' and 'BalancedRegion'
2. The reviewer points out valid concerns about handling multiple classes per image and region sizes
3. The ablation studies show relatively small gaps between different guidance components
4. The contextual similarity approach uses existing models (VGGNet) rather than novel techniques

The method does differ from naive random sampling as claimed, but the significance and uniqueness of these differences is somewhat overstated.

<score>3</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:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Output:
After reviewing the claim and evidence, I find that it is somewhat overstated. While the authors do introduce copy-paste as a synthesis technique for partially-labeled images in incremental segmentation, the evidence shows that copy-paste augmentation has been previously used in various visual tasks (Dwibedi et al., 2017; Ghiasi et al., 2021; Dvornik et al., 2018). The authors acknowledge this by stating they "extend the augmentation strategy from Ghiasi et al. (2021)." What's novel is their specific application and guidance framework for incremental segmentation, not the general concept of copy-paste synthesis.

<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:

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

The review comment to be evaluated is:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Output:
After reviewing the evidence, the claim "We are the first to introduce copy-paste as a synthesis technique to address partially-labeled images for incremental segmentation" appears to be reasonably well-stated. The evidence shows:

1. Previous works focused on pseudo-labeling or modified loss approaches for partially-annotated images
2. While copy-paste augmentation existed before (Dwibedi et al., 2017; Ghiasi et al., 2021), its application to incremental segmentation with partially-labeled images appears novel
3. The review comments acknowledge the novelty and understudied nature of this scenario
4. The paper extends existing copy-paste methods with new guidance techniques specifically for this use case

The claim carefully bounds its novelty to this specific application rather than overstating broader innovation.

<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:

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

The review comment to be evaluated is:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Output:
After reviewing the evidence, I find this claim to be moderately overstated. While the authors do introduce copy-paste as a synthesis technique for partially-labeled images in incremental segmentation, the evidence shows that copy-paste augmentation techniques have been previously studied extensively (Dwibedi et al., 2017; Ghiasi et al., 2021; Dvornik et al., 2018). The authors even acknowledge building upon Ghiasi et al.'s (2021) augmentation strategy. What's novel is their specific application and adaptation of copy-paste for few-shot incremental segmentation, not the fundamental technique itself. The claim would be more accurate if it specified they were first to adapt/apply copy-paste specifically for this use case.

<score>4</score>