Renovating Names in Open-Vocabulary Segmentation Benchmarks

Authors: haiwen  huang
songyou  peng
dan  zhang
andreas  geiger
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-14 13:21:36

Abstract: Names are essential to both human cognition and vision-language models. Open-vocabulary models utilize class names as text prompts to generalize to categories unseen during training. However, the precision of these names is often overlooked in existing datasets. In this paper, we address this underexplored problem by presenting a framework for "renovating" names in open-vocabulary segmentation benchmarks (RENOVATE). Our framework features a renaming model that enhances the quality of names for each visual segment. Through experiments, we demonstrate that our renovated names help train stronger open-vocabulary models with up to 15% relative improvement and significantly enhance training efficiency with improved data quality. We also show that our renovated names improve evaluation by better measuring misclassification and enabling fine-grained model analysis. We provide our code and relabelings for several popular segmentation datasets to the research community on our project page: https://andrehuang.github.io/renovate.

Main paper: 
 introduction: 
 Categorizations which humans make of the concrete world are not arbitrary but highly determined. In taxonomies of concrete objects, there is one level of abstraction at which the most basic category cuts are made.-Eleanor Rosch, Basic Objects in Natural Categories [1] We use names every day. Imagine wandering the trails of a national park -do you pause by a "lake" or simply a body of "water"? While driving through urban sprawl, do you see "trees" or "vegetation" along the road? Our instinctual use of terms like "lake" and "trees" illustrates the human propensity to categorize the world around us into "basic categories" -rich in information and readily identifiable without unnecessary complexity [1, 2, 3] . Such categorization is fundamental to human cognition and communication.In stark contrast, recent advancements in open-vocabulary models [4, 5, 6, 7, 8, 9, 10, 11, 12] struggle to replicate this nuanced aspect of human categorization. While these models have made strides in generalizing to both familiar and novel categories via textual prompts, they are often hampered by the imprecise and sometimes wrong names provided in existing benchmarks. In fact, most datasets are labeled with class names that serve merely as identifiers to distinguish classes within a dataset, rather than descriptive labels aligning with the "basic categories" that match with the actual visual contents. As shown in Fig. 1 , existing names are often inaccurate, too general, or lack enough context, leading to discrepancies between the model's outputs and the actual visual segments. This misalignment indicates a pressing need for a reassessment and refinement of name labeling prac-tices. By adopting a naming scheme that aligns more closely with human categorization, we can pave the way for enhanced open-vocabulary generalization and more accurate model evaluation. Figure 1 : Problems of names in current segmentation benchmarks. We demonstrate examples from well-known datasets: MS COCO [13] , ADE20K [14] , and Cityscapes [15] . Our renovated names are visually more aligned and help models to generalize better.In this work, we focus on renovating the names for open-vocabulary segmentation benchmarks, as their dense annotations pose a greater challenge and offer broader applicability than other recognition tasks. The importance of names in open-vocabulary segmentation is often overlooked, with one exception, OpenSeg [5] , which manually inspected and modified the class names of several segmentation benchmarks, resulting in considerable performance gains of their proposed model. While their work demonstrated the importance of choosing good names, a manual approach is subjective and difficult to scale. To date, there exists no systematic study on how to rename benchmarks in a scalable and principled way. Our work is the first attempt to tackle this challenge.In this work, we introduce a scalable, simple, yet general method for renaming segmentation benchmarks that outperforms manual label efforts like OpenSeg [4] . Our approach leverages foundation models to automate the renaming process, significantly reducing the manual labor traditionally required. Towards this goal, our method can be divided into two steps. First, we narrow down the name search space from the whole language space to a curated list of candidate names. This list is generated by leveraging the original class names with contextually relevant nouns extracted from visual contents via an image captioning model, thereby streamlining the search for names. Next, we employ a specially trained renaming model to identify and select the best-matching candidate name for every ground-truth segmentation mask. In this way, we match an individual name for each instance without any extra human annotations.To demonstrate the value of RENOVATE names, we explore two practical applications. We first use our renovated names for training open-vocabulary models and show that they substantially enhance generalization performance across various benchmarks by up to 15%, indicating a more accurate alignment with visual segments. Our new names also significantly increase training efficiency thanks to their enriched textual information. Second, we apply our renovated names to improve evaluation of open-vocabulary segmentation. Specifically, we reveal that current pre-trained models are making many "benign" misclassifications, i.e., misclassified names that are semantically close to the groundtruth names. RENOVATE names enable more fine-grained model performance analysis, providing valuable information for further model enhancement and dataset curation.In summary, our work makes the following contributions:• We point out the naming problem in existing open-vocabulary segmentation benchmarks.• We propose a scalable, simple, and general framework for automatic dataset renaming.• We demonstrate that our RENOVATE names help to train models with stronger open-vocabulary capabilities and refine evaluation benchmarks, providing valuable insights for future research. 
related work: 
 Open-Vocabulary Segmentation Methods.Prior work in open-vocabulary segmentation [5, 6, 7, 8, 16, 17, 18, 19, 20] focused on adapting vision-language models (VLMs) [21, 22, 23] to the seg-Table 1 : Examples of context names and generated candidate names for three selected classes from ADE20K. Context names are key to comprehending general terms such as "field" and "box" and disambiguating polysemous terms like "cradle", which, in this context, refers to a baby bed rather than a phone cradle or a mining tool.mentation task without compromising pre-trained vision-langauge alignment. These efforts have concentrated mainly on the architecture design, exploring different backbones [24, 25] , mask proposal branch designs [19, 5] , and mask feature extraction techniques [7, 26, 27] . Our work differs from them in that we aim to adapt pre-trained vision-language representations for segment-wise name matching through our novel designs like candidate name generation techniques and our specialized renaming model.Names in Open-Vocabulary Segmentation Benchmarks. Despite its apparent importance, the naming problem in open-vocabulary segmentation benchmarks has not received adequate attention. An exception is the work by [28] , which proposes to learn class-specific word embeddings for improved model adaptation to new datasets. However, these learned embeddings are not in the language space and cannot be used by other models. In addition, some works [29, 30] propose to decompose the class names into a set of attributes or prototypes for open-vocabulary segmentation.To the best of our knowledge, OpenSeg [5] is the only work that proposes to directly improve the quality of the names. However, their approach relies on manual inspection of the datasets, which can be subjective and hard to scale. In this paper, we systematically explore the renaming challenge, culminating in a scalable, simple, yet general framework to overcome these limitations.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes]Justification: In Section 6, we discussed we didn't fully explore all the design choices for renaming pipeline, which we aim to do in the future. Our renaming procedure could also inadvertently propagate biases from the foundation models to the new names. We suggest future users to verify the names for critical applications.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
renovate: renaming segmentation benchmarks: 
 Our method RENOVATE aims to rename the original names in a given dataset with ground-truth mask annotations. As shown in Fig. 2 , we first generate a pool of candidate names, covering potential variants of the original class names. Then, we train a specially designed renaming model that can measure the alignment between segments in pixel space and names in language space. Equipped with the renaming model, we select the top-matching names from the candidate pool for each segment, thereby producing segment-level, refined names that offer enhanced accuracy and details in characterizing the dataset, see Fig. 3 and Fig. 4 .We use GPT-4 [31] for creating a pool of candidate names. A naive solution is to prompt GPT-4 with the original name and ask it to generate synonyms and hierarchical concepts. However, since the original names are often too general and ambiguous, GPT-4 does not have sufficient knowledge to generate high-quality candidates. Therefore, we propose to exploit the visual contents for generating some "context names" (Table 1 ) that assist GPT-4 in comprehending the category's meaning prior to generating candidate names.As shown in Fig. 2 , for each category, we use an image captioning method to process all training images that contain that specific category based on ground-truth annotations. From the generated captions, we further extract nouns by text parsing and filtering as done in CaSED [32] . We observe that nouns appearing more frequently tend to offer deeper insights into the typical environments or traits associated with the category. Therefore, we construct context names by selecting the top 10 most recurrent nouns for each category. We then use them as additional inputs alongside the original class names to prompt GPT-4. LinearX (0) X (l-1) X (l) pix F M i M (L)c (L) . . .X (L)output queries Specifically, we instruct GPT-4 to generate two types of candidate names: (1) Synonyms or subcategories, which can expand the lexical scope of the overly general class name. For instance, the class name "grass" could yield variations like "lawn" or "turf". For this type, synonyms and subcategories are considered together, acknowledging their often interchangeable nature. (2) Providing short, distinct contexts to polysemous names. As an example, the ambiguous name "fan" could be elaborated into "ceiling fan" or "floor fan", offering clearer specificity. As shown in Table 1 , the 5-10 candidate names generated by GPT-4 are more precise than the original names, reflecting the context information encoded by context names. We provide detailed GPT-4 prompts in the supplement.Among the generated candidates, we will only keep those that are better aligned with the visual contents. Particularly, we train a model that can assess the alignment between each name and segment, using segmentation as the proxy task. Conceptually, a candidate name that well describes the segment in the image should help the model recover its ground-truth mask and make a correct classification. Therefore, the model should allow vision-language interaction, and the supervision signal should encourage to use textual information for mask prediction and classification.Our renaming model is illustrated in Fig. 2 . Its vision part starts from a CLIP-based vision backbone followed by a pixel decoder that gradually upscales the backbone features to generate per-pixel embeddings F pix ∈ R H×W ×C . The candidate names of the categories present in the image are processed by the CLIP text encoder. The interaction of textual and visual information takes place at the transformer decoder. The initial queries X (0) are from the text embeddings, and then updated by the transformer decoder consisting of L blocks using the multi-scale visual features from the pixel decoder. As output of each block, the queries X (l) generate intermediate mask and class predictions:EQUATIONwhere * refers to pixel-wise multiplication. The final predictions are made by the queries X (L) . Our pixel decoder, mask prediction and classification follow the design of Mask2Former [33] , which handles panoptic, instance, and semantic segmentation in a unified manner. The key differences lie in 1) the transformer decoder design, and 2) the training strategy, which we detail subsequently. Transformer Decoder. Our transformer decoder uses text embeddings as input queries, unlike most open-vocabulary segmentation models that employ text embeddings solely as weights of the Linear layer in Eq. ( 1) for classification. This key change fosters an earlier integration of text and visual features at varying scales, essential for leveraging text information in segmentation tasks. As shown in Fig. 2 , the text embedding of each candidate name from the segment's class "bicycle" feeds into the transformer decoder, generating their corresponding mask and class predictions.Next, within each block of the transformer decoder, the masked cross-attention (CA) layer adopts ground-truth masks as the attention biases, and the self-attention (SA) layer [34] and feed-forward network (FFN) are the same as in Mask2Former. The queries interact with the visual features at the masked CA layers, which attend within the region defined by the ground-truth mask, i.e., the attention bias. In the example of Fig. 2 , the visual features corresponding to the segment "bicycle" can therefore contribute more effectively to refining the query. To encourage the model to rely more on the textual information in prediction, we further randomly replace the ground-truth masks with predicted masks from the preceding block in the intermediate layers. Importantly, while groundtruth mask based attention biases augment the V-L interaction by focusing on the precise segment regions, they do not reduce the task to a straightforward use of ground-truth mask inputs for mask prediction, as the output queries remain a set of vectors. The final mask prediction M (L) is based on the correlation of these output queries X (L) with the feature map F pix generated by the pixel decoder as in Eq. (1) .Training Strategy. With the CLIP backbone kept frozen, we train the transformer decoder together with the pixel decoder. To recover the ground-truth mask M i and class c i , the transformer decoder makes multiple mask and class predictions with candidate names from the class c i . The prediction with the highest Intersection over Union (IoU) with M i is selected for loss computation:EQUATIONwhere L mask and L class are the mask localization and classification loss functions in Mask2Former. To provide extra supervision on the name quality, we append the candidate names with a "negative" name randomly selected from a different category than the ground-truth class c i . If a "negative" name scores the highest IoU, we supervise the predictions with an empty mask and the "void" class (c void ) which is one extra class in addition to the training classes:L i = L mask (0, Mbest ) + L class (c void , ĉbest ).(3) The "void" class was also used in Mask2Former classification when the prediction mask is not matched to any ground-truth mask in the image. Having both the positive and negative supervision, we effectively incentivize the model to favor names of high quality and penalize those of low quality, thereby aiding in the accurate identification of the best-matching names for each segment.As illustrated in Fig. 3a , to obtain the renovated names, we run the trained renaming model to associate each ground-truth mask with the candidate name. For each segment, our renaming model first ranks the names by the IoU scores between the predicted masks and the ground-truth mask, then selects the top-scoring name as the renovated name. While the IoU ranking scores are not probabilities, they are valuable to understand the relative and absolute model confidence in the names, i.e., how the chosen name stands against the other candidates and whether the selection is indeed confident. In the experiments (Section 5.3), we make use of this information to speed up human verification. We show some examples of renovated names in Fig. 4 and more in the supplements.Our renovated names are readily usable for analyzing the dataset distribution and biases in a more fine-grained manner, as exemplified in Fig. 3b . Note that our renovated names in each original class are not necessarily mutually exclusive because different visual segments may be matched to names at different hierarchies. For example, in Fig. 4 , a person is named as "engineer" (second row), likely due to the clothing, while the other person is named as "woman" (third row), possibly from the physical appearance. But "engineer" and "woman" are not mutually exclusive names. An interesting future extension would be to further study the hierarchies of different names in a dataset.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? Answer: [NA] Justification: This paper does not include theoretical results.Guidelines:• answer NA means that the paper does not include theoretical results.• All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced. • All assumptions should be clearly stated or referenced in the statement of any theorems. • The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition. • Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material. • Theorems and Lemmas that the proof relies upon should be properly referenced. 
applications of renovate names: 
 We further demonstrate the value of the renovated names with two application tasks: training stronger open-vocabulary segmentation models and improving existing evaluation benchmarks. The improvements in both tasks indicate the quality of RENOVATE names.RENOVATE names offer precise and diverse text information. We exploit this to improve model training and achieve better generalization and data efficiency. Following prior works [24, 25, 35] , we replace original ground-truth names with RENOVATE names to compute text embeddings.In addition, we employ the "negative sampling" technique, commonly used in natural language processing to enhance training with a large number of classes [36, 37, 38, 39] . For each segment, we first randomly select C -1 negative classes and choose one RENOVATE name from each. These names are then concatenated with the ground-truth RENOVATE name to form the text prompt. This effectively constructs text embeddings of length C with semantically distinct names, improving training efficiency and generalization. For computing the classification loss, we use the cross entropy function on a per-segment basis, i.e., for an image with segments{m i } N i=1 , L class = N i=1 CE(c i , softmax(v i T T i )), where c i is the ground-truth label, v i ∈ R d is the predicted visual embeddings and T i ∈ R C×d is the constructed text embeddings through negative sampling.Throughout our experiments, we use negative sampling in our model training unless specified.RENOVATE names also enable more fine-grained analysis of open-vocabulary models. For instance, for a misclassification from "car" to "van", we may distinguish between "SUV" to "minivan" versus "sedan" to "delivery van", which isn't possible with class-level annotations in current datasets. However, even with RENOVATE names, standard segmentation metrics obscure our desired fine-grained performance insights since they penalize all such misclassification cases equally. To this end, we adopt "open" evaluation metrics [40] that consider semantic similarity between names. Specifically, for a groundtruth mask pixel g i with label c i and a predicted mask pixel d j with predicted label c j , we compute soft T P ci = S ci,cj , soft F P cj = 1 -S ci,cj , and soft F N ci = 1 -S ci,cj , where S ci,cj is the semantic similarity between c i and c j . These soft metrics are then used to compute panoptic quality (PQ), Average Precision (AP), and mean IoU (mIoU) in a standard way. In Section 5.3, we demonstrate how open metrics effectively reveal that there are indeed many "benign" misclassification scenarios using RENOVATE names.While our renaming process is automated, it is essential to ensure exceptionally high-quality names for evaluation. Therefore, we introduce a rigorous verification process involving five human annotators who review the top three name suggestions and their confidence scores from our model to select the best match. If none of the top three names are suitable, verifiers examine the entire candidate list. In case of disagreements, verifiers discuss and decide collectively. Note that human verification is used only for evaluation sets, while automatically generated names are used for the much larger training sets. Future work could explore using multiple vision-language foundation models to automate this verification process as evaluation sets grow [41, 42, 43] .Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [Yes] Justification: We provide implementation details regarding all our experiments on renaming, training with renovated names, and improving evaluation with renovated names in Appendix C. Guidelines:• The answer NA means that the paper does not include experiments.• If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not. • If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable. • Depending on the contribution, reproducibility can be accomplished in various ways.For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed. • While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example (a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm. (b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully. (c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset). (d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility.In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results. 
obtaining renovated names: 
 Setup. We renovate three panoptic segmentation datasets respectively: MS COCO [13] , ADE20K [14] , and Cityscapes [15] . To generate context names, our default image captioning model is CaSED [32] , which retrieves captions by matching vision embeddings with text embeddings of captions from a large-scale PMD dataset [44] . We train a renaming model for 60k iterations with a batch size of 16 Results. Table 2 summarizes the statistics of RENOVATE names. Compared to the original class names, each dataset has approximately 5-6 times more distinct RENOVATE names, indicating that they provide more diverse and finegrained semantic annotations to the visual segments. We show some visual examples in Fig. 4 and more in Appendix E.1. To further validate the quality of the names, we conduct a human preference study, see Appendix B.1. We also conduct an ablation analysis on the components of the renaming pipeline, see Appendix B.2.Setup. We train FC-CLIP [25] models on MS COCO with our renovated segment-matched names and compare them with models trained using other name sets on COCO, ADE20K, and Cityscapes. Specifically, "OpenSeg" names [5] were manually curated by previous researchers. "Synonym names" are generated by prompting GPT-4 based on the original class names. "Candidate names" are the outputs of the initial step in our renaming process, using image captioning to provide context for prompting GPT-4. A key difference between these names and RENOVATE names is that the former uses a set of names for a class of segments, whereas RENOVATE names provide a name for each individual segment. For model evaluation, we follow prior practices [5, 24, 25] predictions based on the original class divisions. For a fair comparison, our test name set merges names from Original, OpenSeg, and RENOVATE names. 3 demonstrate that using RENOVATE names for training improves segmentation on both source dataset and target datasets. For example, compared to the Original names, the model trained with RENOVATE names improves by near 4 PQ on MS COCO and over 5% mIoU on ADE20K, showing significantly larger gains offered by OpenSeg names. This underscores the value of name refinement on per-segment level and indicates the high quality of RENOVATE names. We also note that models trained with Synonym names and Candidate names show inferior performance even to the Original names, reflecting the importance of high name quality.The richer text information offered by RENOVATE names also improves training efficiency. In Fig. 5 , we show that models trained with RENOVATE names can achieve comparable performance with significantly less data compared to those trained with the Original names. For instance, models trained with 10 4 images using RENOVATE names can match or exceed the performance of models trained with 5 × 10 4 images using the Original names. This corroborates previous findings that improving data quality can reduce the required quantity of training data [45, 46] . Ablation on the negative sampling. In Fig. 6 , we study the effectiveness of negative sampling. Interestingly, we found that negative sampling is especially helpful to RENOVATE names while minorly impacting models trained with original names. This is because RENOVATE names have much more class names and the names are also more semantically close. Negative sampling effectively controls the number of classes used in cross entropy calculation and keeps the name embeddings relatively distinct, thus significantly improving training effectiveness for RENOVATE names.Setup. We evaluate pre-trained FC-CLIP [25] , MasQCLIP [35] , and ODISE [24] models on ADE20K and Cityscapes using three different name sets with both standard and open metrics. For open metric computation, we need the similarity between the ground-truth and predicted class names. For Original and OpenSeg names, which use a set of names for a class of segments, we use the averaged pairwise name similarity between classes. For RENOVATE names, we can directly use the pairwise name similarity between renovated names. Fig. 7 shows PQ evaluation of pre-trained models on ADE20K. Full evaluation results, including mAP and mIoU and results on Cityscapes are provided in Appendix B.3.Results. In Fig. 7 , we first note that the discrepancy between standard and open metrics is significantly greater when evaluated using RENOVATE names. This indicates that many misclassification cases are indeed "benign", i.e., when evaluated using more fine-grained names, the misclassified names turned out to be semantically close to the ground-truth names. For example, when an "area rug" is predicted to be a "floor rug" (RENOVATE names), it will be penalized much less than from "rug" to "floor" (original names); but if an "area rug" is predicted as "concrete floor", the classification error will be even higher. This shows that current pre-trained models have learned relatively good semantic concepts, while still being sensitive to the text prompts at inference time.RENOVATE names also enable a more fine-grained analysis of the models. In Fig. 8a , we present a per-category IoU analysis on RENOVATE names within the ADE20K dataset. We can see that large, well-defined objects such as "sky" and "girl" have high IoU scores, while small, deformable, and rare classes like "bollard" and "ceiling light" are more challenging for current models. Note that most of these names do not exist in the original benchmarks. In addition, our analysis reveals model biases, evidenced by the disparity in IoU scores between "man" and "woman" as well as "girl" and "boy", suggesting an imbalance in the training dataset. In Fig. 8b , we further illustrate how RENOVATE names facilitate a more detailed investigation of misclassifications. For example, among "building" segments misclassified as "wall", 32.7% "churches" from the "building" class are predicted to be "brick wall" from the "wall" class. Additionally, we observe a strong correlation between name similarity and misclassifications. This showcases that our RENOVATE names can help identify problematic sub-categories within the original class that models still struggle with.Finally, we note that the open metrics are penalizing more on misclassified concepts with lower semantic similarity, e.g., "car/road" mistakes are penalized more than"car/truck" mistakes. In other use scenarios, different types of mistakes (e.g., outlier patterns) may be preferred to be penalized more and the evaluation metrics should be desgined accordingly. However, regardless of the design of the evaluation metric, RENOVATE names make it possible to conduct fine-grained analysis on mistakes with different semantic distances. Without RENOVATE names, we can only see the coarse misclassifications without a fine-grained understanding of the model.Question: Does the paper provide open access the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?Answer: [No] Justification: We will release our code upon paper acceptance. The datasets used in this paper are all publicly accessible. For our data processing on MS COCO, ADE20K, and Cityscapes for open-vocabulary segmentation, we follow prior practice [25] , whose code is already publicly available.Guidelines:• The answer NA means that paper does not include experiments requiring code. • Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 
conclusion and limitations: 
 In this work, we address the naming issues and show that renaming improves both model training and evaluation of open-vocabulary segmentation. While RENOVATE uncovers model biases, it could inadvertently propagate biases from the foundational models into the new names. To mitigate potential negative societal impacts, we advocate for verification of names in critical applications, as exemplified by our verification of names in evaluation sets. As the first attempt to propose a generalized renaming framework, we acknowledge that our exploration remains incomplete. In the future, we aim to further refine our method, exploring more design choices such as other model backbones [47] , and scale it up to the publicly available large-scale datasets [48, 49] .Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?Answer: [Yes] Justification: See Appendix C.Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
experiment statistical significance: 
 Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?Answer: [No] Justification: The experiments in the paper are costly. Due to resource and time constraints, we didn't investigate the statistical significance aspect.Guidelines:• The answer NA means that the paper does not include experiments. • If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics. • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction). 10. Broader Impacts Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed? Answer: [Yes] Justification: We discussed broader impacts in Section 6 that while RENOVATE names improve the quality of public datasets, it could also propagate the biases from foundation models to the renovated names. We therefore suggest future users to verify the names for critical applications.Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [Yes] Justification: In Section 6 we discussed how name verification as we conducted in Section 4.2 can potentially prevent harmful model biases being propagated into renovated names.Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort. 12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] Justification: See Appendix F. Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL.• Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly this in the paper. • We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. • For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review. 
 
 Appendix: This supplementary material to the main paper "Renovating Names in Open-Vocabulary Segmentation Benchmarks" is structured as follows:• In Appendix A, More Literature Review, we provide a brief overview of works that address name problems in open-vocabulary classification.• In Appendix B, More Experiment Results, we provide more experiment results, including a human preference study on the name quality, additional ablation analysis on the renaming pipeline, full results on improving evaluation using renovated names and some more examples of using RENOVATE names to conduct fine-grained misclassification analysis.• In Appendix C, More Implementation Details, we elaborate on the implementation details on the GPT-4 prompts for name generation, human verification process for RENOVATE names on evaluation sets, open metrics, and the renaming and pre-trained models.• In Appendix D, More Name Examples, we present more examples of context, candidate, and renovated names. We also show more examples of name selection with the IoU scores.• In Appendix E, More Qualitative Analysis, we provide more qualitative analysis demonstrating that RENOVATE can refine original names, correct wrong annotations, and uncover segments with shared semantic concepts across datasets.• In Appendix F, License for Existing Assets, we list the names of the license for each assets used in this paper. A More Literature ReviewIn this section, we provide a brief overview of name problems in open-vocabulary classification.While OpenSeg [5] is the only prior work for the name problems in open-vocabulary segmentation, there are more research works on improving name quality for open-vocabulary classification. For example, [50] improve class names by including their parents and children from the WordNet hierarchy [51] . [32] propose to replace the fixed vocabulary list with nouns extracted from best-matching captions. [52] improve the descriptiveness of the vocabulary by adding class descriptions. However, since the VLMs used by these models are all trained on image-text datasets, it is non-trivial to extend them to dense prediction tasks like segmentation. For the renaming strategy, our context name design and LLM-based generation approach ensure the diversity of our names with hierarchical concepts as well as descriptive contexts, without the use of an external word hierarchy as [51] or class descriptions as [52] . Our renaming model further improves the name quality based on the visual segments and is more specifically designed for the segmentation tasks.As demonstrated in Fig. B .1, we study the name quality of our renovated names with a human preference study on ADE20K and Cityscapes datasets, which consists of 30249 and 14670 segments in the validation set, respectively. Specifically, we first randomly select 10% of all the segments in the validation set of each dataset. The average time for human experts to finish the preference study is about 2 hours. Then for each segment, we ask 20 researchers unfamiliar with the project about their preference between its original name and our model-selected name. Our results demonstrate that our renovated names are preferred in 82% cases, showing a clear advantage over the original names. We provide more details of our human study in the supplements.To conduct ablation study on the renaming pipeline, we leverage pre-trained open-vocabulary model models as evaluators of names, automating the name quality evaluation process. Instead of involving humans, we prompt pre-trained open-vocabulary segmentation models with different names. Names are then considered to be better if they help the model achieve better open-vocabulary segmentation. Specifically, we use pre-trained FC-CLIP model for the following experiments.In Table B .1, we first analyze the impact of context names for candidate generation. Our results indicate significant performance improvements when context information is provided, as opposed to the "None" approach, which relies only on original names. This highlights the effectiveness of leveraging contextual insights for generating superior candidate names. In comparison, CaSED outperforms both BLIP2 and RAM, achieving a greater boost in performance through its retrieval- based approach and the utilization of expansive external image-caption datasets, underlining the benefits of contextual enrichment in name generation.In Table B .2, we analyze the design choices of our renaming model. We start by establishing that our approach using text queries is superior to a standard text classifier, which only interacts with the transformer decoder's output query embeddings and treats the mask prediction branch in a classagnostic way. In comparison, our text queries interact intimately with spatial details from image features and exploit the mask prediction branch, leading to more precise name-segment matching. We also observe performance benefits from randomly substituting ground-truth masks with predicted ones in attention biases ("rand gt mask"), which suggests training with increased reliance on text queries refines the matching quality. Furthermore, augmenting our model with negative name appending ("text w/ neg") enhances its discriminative capacity, further boosting performance and solidifying the renaming model's overall effectiveness.In Table B We use GPT-4 ChatCompletion API [55] for candidate name generation. For each class, we provide two messages. The first message, i.e., "system message", describes the role of the system, where we describe our overall problem setup and goals. The second message, i.e., "user message", describes the intention of the user, where we provide the original class names and context names. We show our exact prompt messages as follows:System message: You are a helpful assistant aiding in renaming dataset classes. Each class has an inadequate original name and a set of context names derived from related captions (with their frequencies sorted and listed in brackets). These context names provide insights into the category's essence. When renaming, you may: To ensure the high quality of names on the evaluation sets of ADE20K and Cityscapes, we verify the names on all the segments. As shown in Fig. C .1a, for each segment, we provide the model-selected name along with the other top 3 names ranked by the model, we also provide the rest of the candidate names in the "others" option. Therefore, the human verifier can quickly continue to the next segment if the selected name itself or another name in top 3 names matches the most with the segment. If none of the top 3 names fits well, humans need to choose from the other candidate names in "others". In the latter case, it would be similar to the scenario where RENOVATE suggestions are not provided and humans have to choose from the whole un-ranked candidate name list. In Fig. C .1b, we compare the name verification speed with and without RENOVATE suggestions on 200 segments. As we can see, RENOVATE suggestions significantly speed up our verification process. We note that the human verifiers are provided only with candidate names from the same original class, which is typically the original ground-truth class. In the rare cases where our renaming model prefers names from classes other than the original groundtruth class, we first verify the class choices, then provide the human verifiers with the name suggestions from the verified class.We further note that for all the segments verified, 68% of them are matched to the default, i.e., top 1, model-selected name, and 92% of them are matched to the top 3 RENOVATE suggestions, reflecting the high quality of RENOVATE names. In Fig. C .2, we show typical cases when humans and models disagree, including segments that are too small to recognize and segments whose names cannot be inferred correctly due to insufficient visual cues. We note that such segments only consist a minority of all the segments, with inherent difficulty to name precisely.To compute the open metrics [40] , we need a similarity function on names. Previous research [40] compared three types of similarity measures: WordNet methods [56, 57, 58] , text embedding models [36, 59, 60] , and pre-trained language models [23, 61, 62, 63] . They demonstrated that both WordNet methods and text embedding models compute reasonable similarity measures while pretrained language models tend to overestimate similarity between names. However, since the names in our work are in free form and may not map to any synonym sets in WordNet [51] , we adopt one of the text embedding models, FastText [60] , which is shown to compute reasonable similarity measures [40] .More details on the renaming model. For the backbone of the renaming model, we use ConvNeXt-Large CLIP [23, 64] from OpenCLIP [64] pretrained on LAION-2B dataset [65] . Following shows when there is a lack of sufficient visual cues to infer the names of the segments, the choices are ambiguous. For example, both "river" and "lake" are reasonable choices for the segment in the top right image without more information of the scene. Note (a) and (b) consist only a minority of all the segments.Mask2Former [33] , our transformer decoder consists of nine blocks and we use AdamW [66] optimizer with a learning rate of 10 More details on the pretrained models. ODISE [24] provides two pretrained models, ODISE (label) and ODISE (caption), trained on category names and caption nouns respectively. We use ODISE (label) since they demonstrated stronger performance than the other model in the original paper. For ODISE and FC-CLIP, we set their geometric mean parameters α = β = 0.5 so we do not differentiate the treatment of test names based on their name overlapping with the training names.In this way, we can compare different test names in a fair way. By default, we use FC-CLIP for all of our ablation studies. For the test prompt template, we follow prior work [9, 35, 25] to use the ViLD template [9] , which consists of 63 prompt templates such as "There is {article} {category} in the scene." These templates are ensembled by averaging the text embeddings.In Table D contexts to names with multiple potential meanings like "counter" and "bar". We can also see that RENOVATE names provide more comprehensive descriptions of the corresponding classes.In RENOVATE uncovers segments with shared semantic concepts across datasets. Since RENO-VATE aims to describe visual segments using more human-aligned language, it effectively standardizes the naming conventions for identical concepts in different datasets. In Fig. E .4, we demonstrate this with examples from MS COCO and ADE20K. While renaming each dataset separately, we observe that RENOVATE inadvertently reveals the shared name spaces by identifying more appropriate names in natural language to describe the visual contents. This property can be potentially used for analyzing the differences between datasets, merging datasets to construct new benchmarks, and various other data curation processes.RENOVATE can rename segments in an image when combined with other foundation models.In Fig. E .5, we showcase another possible application of our renaming model by further applying it on generated masks from SAM2 model [67] (points per side=12) and generated image tags from the Recognize-Anything-Model (RAM) [49] . Specifically, we use the generated masks as attention biases and each mask is paired with the full list of the generated tags as the text queries. The renaming model is used to obtain the best-matching name for each mask.The good mask-name matching performance demonstrates that our renaming model can potentially be used on datasets where mask annotations are not available.• CaSED code and model [32] : MIT License.• GPT-4 model [31] : https://openai.com/policies/terms-of-use/ • FC-CLIP code and model [25] : Apache License 2.0.• ODISE code and model [24] : Custom license (https://github.com/NVlabs/ODISE/ blob/main/LICENSE) • MasQCLIP code and model [35] : Custom license (https://github.com/mlpc-ucsd/ MasQCLIP/blob/main/LICENSE) • MS COCO [13] : Creative Commons Attribution 4.0 License. • ADE20K [14] : Creative Commons BSD-3 License Agreement.• Cityscapes [15] :Custom licence (https://www.cityscapes-dataset.com/ license/) Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.38th Conference on Neural Information Processing Systems (NeurIPS 2024). 


Decision: In this paper, the authors presented a renaming model for open-vocabulary segmentation. Due to the precision concern of the class names in existing datasets, the authors were motivated to enhance their quality for visual segments. Experiments show that the renovated names help the training of stronger models for open-vocabulary segmentation. The main contribution of this paper is the study of the naming precision of existing datasets for the open-vocabulary segmentation task, which could benefit follow-up research in this field.

The paper received comments from three expert reviewers, and all of them acknowledged the contributions of the important problem that this paper looked into, and the corresponding renaming approach. However, there were some concerns around the unclear design of the approach and motivation, missing comparison to alternative methods/baselines, and experimental evaluations, leading to a mixed recommendation of both positive and negative. After the rebuttal together with a few rounds of discussions among the authors, reviewers and AC, the above major concerns were addressed and the reviewers agreed on the contributions of this paper and its potential impact on the field.

Considering the above-mentioned points, the discussions, and the potential interests of the paper may have on the NeurIPS audience, the AC recommends Accept, while reminding the authors to include the additionally provided clarifications and revisions during the rebuttal to the final version of the paper.

Reviews: review: paper_summary: paper_summary: The paper considers the problem of open-vocabulary segmentation. The task requires segmentation models to recognize categories outside of the training taxonomy. This usually means learning a joint image-text feature space and classification via similarity matching between class text embeddings and segmentation mask embeddings. The paper aims to improve the suboptimal text descriptions of the dataset classes, which are often too general, ambigous and context dependant. The proposed method describes a strategy for automated renaming of the assigned class descriptions at the segment level. This means that each ground truth segment gets assigned with one or more text descriptions that describe that particular segment more specifically. Training with such enriched text descriptions improves the model performance on both the source and target datasets. The experimental study follows the usual setup where COCO is used as source dataset, and ADE20k and Cityscapes as target datasets.
strength_weakness: strength_weakness: Some parts of the paper remain unclear. 

For example, I am confused about the batch collation for training the renaming model. Figure 2 suggests that the transformer decoder in a single forward pass receives candidate names from a single ground truth class. Does this mean that for a single image the number of forward passes through the transformer decoder is equal to the number of ground truth segments? Would you say then that a single training example is consisted of an image, a single ground truth segment and multiple candidate names? Is then a batch size equal to the total number of segments in the sampled images? Does the batch size 16 from line 239 refers to the number of images or number of segments? Does this type of training affects the memory requirements significantly? Are there any redundancy in terms of the tensor representations that have to be kept in the memory in order to enable parallel training on multiple segments? I suggest authors to clarify these questions.

I also dont understand the details of using renovated names during the evaluation. Does this mean that all the candidate names are just appended to other naming variants of that particular class? It seems to me that we do not need to train the renaming model if we would like to use the renovated names just for the evaluation. Is this correct? How many naming variants per class do you get on average when you concatenate the proposals from the original set, the OpenSeg and the renovated names? Does this affect the inference speed significantly? If you also use the usual templates (a picture of *class name*, an image of *class name*, etc.) it seems to me that there might be a lot of different textual representations for a single class. How does this inference really work?

The renaming model should be compared to some stronger baseline which also has the ability of candidate selection. For example, you could assign each segment with a candidate name that achieves highest similarity with the segment representation based on the CLIP embeddings. The segment representation could be average-pooled feature representation from the CLIP visual encoder. I question the necessity of training the mask prediction module just for renovating the class name associated with some segment, if we already know the corresponding ground truth mask. Could you please comment on this issue and maybe discuss some possible alternatives?

In Table 3. the results obtained with OpenSeg names should correspond to the original FCClip results. However, in some cases the reported performance is lower than the FCClip performance reported in the official repository. Why is this the case?

As shown in Table 3., training with renovated names leads to marginal gain in some cases (e.g. PQ on Cityscapes). 

I am not sure if the x-axis on figure 5 shows the number of images available in the training dataset, or the number of iterations trained on the full dataset? If these experiments consider subsampled training subsets, you should explain how the training images were selected.

I am a bit surprised about the effectiveness of the negative sampling. What would be the effect of including additional renovated names of the ground truth class as negatives? This reminds of hard example mining, as it would force the model to differentiate between similar semantic concepts.
questions: questions: Please consider the questions from the weaknesses section that would clarify training of the renaming model and the evaluation procedure with the renovated names. Also, please discuss the possibility of evaluating the renaming model with another baseline with candidate name selection ability. Please find the suggestion in the weaknesses section, which considers a baseline based on pooling of CLIP features.
limitations: limitations: The authors discussed the limitations in the section 6. The main concern is regarding the bias transfer from the foundation models.
paper_summary: paper_summary: The paper explores modifying the class names associated with mask annotations in the segmentation datasets in the context of open-vocabulary segmentation. A method, RENOVATE, is described to perform such renaming in an automated pipeline. RENOVATE leverages an image captioning model to generate contextual words for each class. GPT-4 is prompted by class name and the contextual world to generate a pool of candidates. A segmentation model is trained to select among the candidate names using the mIoU as a proxy metric. The refined class names are then used to train open-vocabulary models, which show improvement in performance.
strength_weakness: strength_weakness: 1) The section on the training of the ranking model to select among the candidate names could be clearer and more streamlined. It is not particularly clear whether a model is trained for each category or if a single model is trained for all categories. 
   - If it is the former, then there are certainly some computational concerns and limitations that the method imposes. It would be useful to discuss the computation cost wrt to the number of classes in the data.
   - If it is the latter, then the ability to use or re-use this trained model for the downstream task of OV segmentation could be considered. It seems that the effect of gt-mask attn bias is limited (B.2) as randomisation performs better. Also, reporting the performance of this model on the downstream task is of interest.
2) In the same section, the design and presentation of a specialised architecture for the renaming could be better motivated. Since a proxy of a segmentation task is employed, is the new architecture even necessary? Could existing model architecture perform this task? Why do existing OV segmentation model pipelines not meet the requirements of the proposal's renaming stage?

3) The evaluation of the study for the improved performance of the segmentation models is limited -- only a single architecture is explored. Other architectures would help rule out particular drawbacks of the FC-CLIP being addressed with renaming and would provide more evidence to the broader conclusion about renaming helping model training (L310).

4) While the new names might help identify particular problems in models (L297), the reasoning behind renovating names in evaluation is rather backwards. While some misclassifications might be due to the similarity in labels, such as those identified as "benign" by the authors, they are still misclassifications as defined by the task. This then creates a problem in downstream use. For example, two masses in an X-ray might appear very similar due to both being a collection of cells, but their placement and small characteristic outline patterns will determine the difference between benign and dangerous growths. Thus, one should seek to highlight key errors in the evaluation instead, even if the labels are close. It seems that a different phrasing of the conclusions (L296): the learned semantics are not sufficient to differentiate between semantically adjacent classes.

5) [Minor] Finally, the proposed pipeline itself might be less applicable outside of the academic where established datasets with limited label sets are used. Would gathering a dataset with varied captions from the onset make the RENOVATE method redundant?
questions: questions: - It would be good if the issue surrounding the training of the renaming model (W1) could be clarified in the rebuttal. 
 - Additionally, providing some commentary on the weaknesses (W2) and (W4) would help improve the interpretation of the paper. 
 - Furthermore, it would be good to discuss future applicability of the pipeline, or whether the better path is to just collect more precise captions for training data. 

Overall, the paper is well-written and presents some insights into the treatment of data for open-vocabulary segmentation. There are some limitations in the evaluation. I currently rate the paper as BA prior to rebuttal.
limitations: limitations: The paper touches on the potential to propagate existing biases in the foundational models it employs, which is a big issue, and it is important that it is mentioned. 

I would recommend conducting an ethics review or obtaining IRB approval for the human study, even if it is not strictly necessary according to the respective institutional guidelines.
paper_summary: paper_summary: Open-vocabulary models use class names as text prompts for unseen categories’ generalization during training. In this paper, the issue of imprecise or even wrong class names from existing datasets is specially studied. One simple and general framework is proposed for automatic dataset renaming, that is, first narrow down the whole name search space to a curated list of candidate names, by using original class names with contextually relevant nouns from image captioning; next employ a specially trained renaming model to select the best-matching candidate name for every ground-truth segmentation mask. To demonstrate the effectiveness, two types of practical applications are explored, i.e. using renovated names to train open-vocabulary models, and applying renovated names to improve evaluation of open-vocabulary segmentation.
strength_weakness: strength_weakness: [-] Noise type & noise rate. The authors partially visualize the noise issue of some class names, such as inaccurate or too general or lack contexts, which is worth encouraging. But, are there only these three types of noise in real-world scenarios? What are the noise rates for different datasets? And is the effectiveness of RENOVATE the same for different types/ratios of noises?
Thorough statistics are needed to clarify the performance bounds.

[-] How many ground-truth masks are needed when sorting candidate names using visual context? How to ensure consistency in visual alignment, as visual masks typically have significant intra-class variances (such as humans having different hairstyles)?

[-] To obtain candidate names, GPT-4 and image captioning are used. For GPT4, it is usually sensitive to prompts, how to ensure the quality of generating candidate names (Quantitative comparisons are needed)? Do you need additional labor to perform a double-check after combining results from GPT-4 and captioning?

[-] Since renovating names for open-vocabulary understanding seems to be one general idea, why only focus on segmentation tasks? Will the conclusion of this paper be exactly the same for classification or detection? For example, when using visual samples to match the best candidate, the intra-class visual variances of classification are definitely greater than those of segmentation.
questions: questions: [-] In the related-work section, some citations are missing, which may mislead readers/reviewers. [1,2] have explored the text issue of class names in open-vocabulary segmentation, with some overlapping with this paper. The reviewer encourages including them and conducting detailed discussions and comparisons.

[1] AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation.      NeurIPS 2023

[2] Multi-Modal Prototypes for Open-Set Semantic Segmentation. Spring IJCV
limitations: limitations: As the authors said, RENOVATE could inadvertently propagate biases from the foundational models into the new names. To mitigate potential negative societal impacts, they advocate for verification of names in critical applications.

Also, the authors acknowledge that, the exploration remains incomplete, and will further make refinement to explore more model backbones and scale up to large-scale datasets.

Comments: title: Thanks again to all the reviewers
Thanks for your review
Thanks for your response and score upgrade
Thanks for the response
Thanks for the Response
Thank you for the rebutall
comment: As the discussion period concludes, we would like to express our sincere gratitude to all the reviewers for their valuable feedback and suggestions. We are pleased to see that all the reviewers acknowledged our rebuttal effectively addressed their concerns and subsequently raised their recommendation ratings, recognizing the value of our paper. We will carefully consider all the feedback and incorporate it into our revised paper.
We thank the reviewer for the ethics review. As the reviewer mentioned, in our paper, we have acknowledged that bias propagation from foundation models is indeed an issue and we advocated for verification of names in critical applications. In case the reviewer sees further issues, we would be happy to address them.
Thank you for taking the time to review our response. We’re pleased that our rebuttal has addressed your concerns and we sincerely appreciate the recommendation score upgrade. We will carefully consider and incorporate the key points you've raised as we work on the revised paper.
I would like to thank the authors for the rebuttal. Most of my concerns were addressed. Thus, I will upgrade my decision to weak accept.
We thank the reviewer for the response and we are pleased to see that our rebuttal has addressed the reviewer's concerns.

If any remaining concerns hold the reviewer’s opinion on the current borderline recommendation, we would be happy to provide further clarification and discussion. Otherwise, we invite the reviewer to kindly raise the recommendation rating.

Update: We have noticed the recommendation score raising and would like to thank the reviewer again for recognizing the value of this work.
The reviewer thanks for the efforts made during rebuttal, and most of my issues has been solved.
Thank you for taking the time to review our response. We’re pleased that our rebuttal has addressed your concerns and we appreciate the recommendation score increase. We will make sure to incorporate the reviewer's suggestions into our revised paper.
I wanted to acknowledge and thank the authors for their detailed responses. I believe the majority of my concerns have been addressed, and I support the proposed changes. I will update my recommendation accordingly.