DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition

Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-14 09:25:05

Abstract: As one of the fundamental video tasks in computer vision, Open-Vocabulary Action Recognition (OVAR) has recently gained increasing attention, with the development of vision-language pre-trainings. To enable open-vocabulary generalization, existing methods formulate vanilla OVAR to evaluate the embedding similarity between visual samples and text descriptions. However, one crucial issue is completely ignored: the text descriptions given by users may be noisy, e.g., misspellings and typos, limiting the real-world practicality. To fill the research gap, this paper analyzes the noise rate/type in text descriptions by full statistics of manual spelling; then reveals the poor robustness of existing methods; and finally rethinks to study a practical task: noisy OVAR. One novel DENOISER framework, covering two parts: generation and discrimination, is further proposed for solution. Concretely, the generative part denoises noisy text descriptions via a decoding process, i.e., proposes text candidates, then utilizes inter-modal and intra-modal information to vote for the best. At the discriminative part, we use vanilla OVAR models to assign visual samples to text descriptions, injecting more semantics. For optimization, we alternately iterate between generative-discriminative parts for progressive refinements. The denoised text descriptions help OVAR models classify visual samples more accurately; in return, assigned visual samples help better denoising. We carry out extensive experiments to show our superior robustness, and tho rough ablations to dissect the effectiveness of each component.

Main paper: 
 introduction: 
 Action recognition is one of the fundamental tasks in computer vision that involves classifying videos into meaningful semantics. Despite huge progress that has been made, existing researches focus more on closed-set scenarios, where action classes remain constant during training and inference. Such scenarios are an oversimplification of real life, and thus limiting their practical application. Recently, another line of research considers one more challenging scenario, namely open-vocabulary action recognition (OVAR), and receives increasing attention.OVAR allows users to give free texts to describe action classes, and the model needs to match novel (unseen) text descriptions to videos with similar semantics. To tackle OVAR task, Vision-Language Alignment (VLA) paradigm [41, 14, 57] provides one preliminary but popular idea, i.e., measuring the embedding similarity between text descriptions and video embeddings. Following this paradigm, recent works focus on minor improvements, e.g., better align vision-language modalities [16, 49, 62] .Although promising, these works all maintain one unrealistic assumption in real-world scenarios, i.e., the given text descriptions are absolutely clean/accurate. The concrete form is that they evaluate openvocabulary performance by re-partitioning closed-set datasets in which text descriptions of classes are fully human-checked. But in fact, under real-world OVAR, novel text descriptions provided by users are sometimes noisy. Character misspellings (typos, missing, tense error) are inevitable [43, 25] in Submitted to 38th Conference on Neural Information Processing Systems (NeurIPS 2024). Do not distribute. For open-vocabulary action recognition (OVAR), existing researches neglect an essential aspect: the text descriptions provided by users may be noisy (e.g., misspelling and typos), resulting in potential classification errors and limiting the real-world practicality. Right: Rethinking the robustness for popular OVAR methods [49, 62] . On various datasets, they exhibit high sensitivity to text noises. Besides, as the noise level increases, the performance degrades significantly. thousands of descriptions, since users often don't double-check, as well as differences in user habits and diversity of scenarios (Fig. 1 Left).We are hence motivated to fill the research gap of noisy text descriptions in OVAR. We analyze the noise rate/type in real-world corpora [26, 45, 3] . We also make comprehensive simulations of text noises, following NLP literature [42, 47] . Fig. 1 Right empirically evaluates noise hazards for existing OVAR methods [16, 49, 62] . One can find that just a small amount of noise lowers recognition accuracy by a large margin, implying quite poor robustness.To spur the community to deal with the noisy OVAR task, being necessary and practical, this paper bravely faces the challenges. One vanilla idea is using a separate language model (e.g., GPT [1]) to correct noisy class descriptions, and then adapt the off-the-shelf vision-language paradigm [41, 14, 57] .However, there exist two nettlesome issues. 1) Textual Ambiguity. One text description is usually a few compact words, with vague semantics, e.g., for the noisy text "boird", there could be multiple cleaned candidates in terms of spelling, such as "bird" and "board". This short text lacks context, making phrase correction difficult for uni-modal language models. 2) Cascaded Errors. Text correction and action recognition are independently completed, without sharing knowledge. The noisy output of text correction is cascaded to the input of action recognition, resulting in continuous propagation of errors. To address these issues, we design one multi-modal robust framework: DENOISER.Our first insight is to treat denoising of text descriptions as one generative task: given noisy text descriptions, decode the clean ones, by considering text-vision information to help denoising. Specifically, it consists of three components: text proposals, inter-modal weighting, and intra-modal weighting. We first propose potential text candidates based on spelling similarity to limit the decoding space. Then, two types of weighting are combined to decide the best candidate, that is, inter-modal weighting uses assigned visual samples to vote; while intra-modal weighting relies solely on text information. Our other insight is employing existing OVAR models as off-the-shelf tools to assign visual samples at discriminative step. Such tools have been proven to handle clean OVAR tasks well, also making our framework easier to adapt to previous models. For full usage of information in the same semantics, we then assign detail-rich visual samples to clarify the semantic ambiguity of compact text descriptions. To further avoid cascaded errors, we propose a solution of alternating iterations, to connect generative and discriminative steps. By progressive refinement, denoised text descriptions help OVAR models to match visual samples more accurately; assigned visual samples help better denoising. Under multiple iterations, denoising results and OVAR are both better.Our main contributions are summarized as follows:• We pioneer to explore noisy text descriptions for open-vocabulary action recognition (OVAR): first fully analyze the noise rate/type in text descriptions by extensive statistics in real-world corpora; then evaluate the robustness for existing methods; finally rethink to study one practical task: noisy OVAR.• We propose a novel DENOISER framework to tackle the noisy OVAR task, by alternately optimizing generative-discriminative steps. The generative step leverages knowledge of vision-text alignment to denoises noisy text descriptions, in the form of progressive decoding; while the discriminative step assigns visual samples to text descriptions for open-vocabulary action recognition.• We carry out extensive experiments to show the superior robustness of DENOISER against noisy text descriptions, under various noises and datasets. Great performance improvements are achieved over existing competitors. Thorough ablations are studied to show effectiveness of every design. 
related work: 
 Vision-Language-Audio Pre-training (VLP) aims to jointly optimize multi-modal embeddings with large-scale web data, e.g., CLIP [41], ALIGN [14] , Florence [57] , FILIP [55] , VideoCLIP [52] , and LiT [58] . In architectures, VLP uses independent encoders for vision, text, and audio, followed by cross-modal fusion. For optimization, contrastive learning [5, 61] and cross-modal matching [7, 29] are mainstream, covering self supervision [32, 34] , weak supervision [28, 8] and partial supervision [19, 33] . VLP benefits various applications: image-text retrieval [6, 18] , video understanding [23, 20, 22, 21] , action recognition [16, 60] , visual grounding [32, 56, 31] , AIGC [4, 36] .Open-Vocabulary Concept Learning aims to understand vision, where conceptual semantics are described by free/arbitrary text descriptions. It is characterized by using vision-language pre-trainings to match text descriptions and visual samples in semantic space. Its typical evaluation metric is the downstream zero-shot performance, i.e., classify unseen classes [49, 62, 17, 38, 54, 48, 37] . To achieve the evaluation, most methods re-partition closed-set datasets. [49] Although there is some plausibility, such re-partition implicitly makes an unrealistic assumption: text descriptions of unseen classes are human-checked, and thus absolutely clean, limiting real-world application. We pioneer taking noises from text descriptions (misspellings and typos) into consideration. By adding real-world noise for the above methods, we reveal their poor robustness, and design DENOISER for solution.Robustness of Language Models is extensively studied by adversarial attack-defense techniques [50, 59] . When text inputs are facing noises, defense methods correct the outputs, dividing into: detectionpurification [63, 39] , as well as adversarial training [53, 9, 35, 30, 51] . The former methods detect and correct the corrupted part of a text phrase. The latter trains a model on adversarial samples to increase its direct noise-against ability. Overall, all these methods employ solely textual information for robustness in pure NLP tasks. We differ from them by considering robustness in the context of multi-modal scenarios and by employing multi-modal information to better assist text denoising.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes] Justification: We discuss the limitation of our method at the end of the paper, and in the appendix.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. 
method: 
 We explore noisy text descriptions for open-vocabulary action recognition. In Sec 3.1, we introduce noisy open-vocabulary setting; in Sec 3.2, we detail our DENOISER framework, covering generative discriminative sub-parts; in Sec 3.3, we report the accompanying optimization strategy.Open-Vocabulary Action Recognition (OVAR). For a video datasetV = (v j ∈ R T ×H×W ×3 ) N j ,OVAR aims to train one model Φ OVAR that matches target videos with arbitrary text description T .EQUATIONwhere Y refers to the matching label between V and T . During training, (video, text, matching label) triplets from the base semantic-classes are provided; while during testing, the model is evaluated on the novel semantic-classes. Note that, the semantic-classes between training (C base ) and testing(C novel ) are disjoint, i.e., C base ∩ C novel = ∅.To enable open-vocabulary capability, recent OVAR studies [16, 49, 62, 40] embrace vision-language pre-trainings (VLPs), for their notable ability in crossmodal alignment. Specifically, OVAR could be achieved by measuring the embedding similarity between text descriptions T and video samples V, which is formally formulated as:EQUATION)where σ refers to the softmax activation, Φ pool is the spatio-temporal pooling, Φ vis and Φ txt are visual and textual encoders of VLPs, D is the embedding dimension.Noisy Text Descriptions in OVAR. Although great progress has been made, the VLA paradigm suffers from an unrealistic assumption, i.e., that text descriptions are absolutely clean/accurate,Better Texts, Better Results We first propose text candidates Φ prop for T i-1 based on spelling similarity; then choose the best candidate by inter-modal weighting Φ inter and intra-modal weighting Φ intra . Φ inter uses vision-text information, while Φ intra relies solely on texts. Ψ disc assigns text semantics to visual samples, then only visual samples with the same semantics can vote for text candidates. We optimize alternatively between generative and discriminative steps to tackle noisy OVAR. limiting the practicality in reality. Actually, the diversity of users and scenarios can easily cause text descriptions given to be somewhat noisy, especially for unseen semantic-classes, due to their enormous degree of freedom. Formally, for one text description with n words, the clean/noisy versions T /T ′ are:EQUATION)where t i is the i-th word of T .Ψ noise refers to noise contamination in reality, e.g., inserting, substituting and deleting characters with probability p, following [42, 47] . Since these three atomic operations defined in Levenshtein edit distance D are of distance 1, noise rate p can also be deduced by:EQUATIONAs a result, the noisy OVAR task can be formulated as: given V and T ′ , the model is expected to maximize the accuracy of action recognition, and even recovering T ′ to T .Robustness of Existing Methods. Fig. 1 evaluates for typical OVAR studies [49, 62] , across three public datasets. In terms of Top-1 classification accuracy, existing methods are rather sensitive to noise and show one trend: the larger the noise, the more significant the performance degradation (please see quantitative experiments in Tab. 2). Such poor robustness to the noisy OVAR task, proves excessive idealization of existing studies and also motivates us to fill the research gap.Motivation. Given the complexity of noisy OVAR, we here divide it into two sub-steps: denoising of text descriptions, and then vanilla OVAR. The former is viewed as one generative decoding form, by considering both vision-text information for progressive denoising. While the latter is in one natural discriminative form, by assigning text descriptions to video samples. For the joint optimization of these two sub-steps, we iterate alternately between generative and discriminative forms. As a result, our DENOISER framework progressively tackles the noisy OVAR task.Framework. As shown in Fig. 2 , our DENOISER framework covers two components: generative sub-step Ψ gene and discriminative sub-step Ψ disc . For Ψ gene , we iteratively refine text descriptions by one decoding process, that is,(T 0 , T 1 , • • • , T n ),where n is the index of decoding steps. Upon finishing step i, we will haveT i = (t 1 , • • • , t i , t ′ i+1 , • • • , t ′ n ), where t refers to the decoded version of t, meaning that the i-th word of text descriptions is decoded at step i. We start with T 0 = T ′ , and finish at T n to ensure that all words are denoised. While for Ψ disc , we find it identical to vanilla OVAR task and thus leveraging the VLA pipeline [16, 49] for help, which is off-the-shelf and well-studied.Formally, our DENOISER framework tackles noisy OVAR as follows:EQUATIONAt the discriminative step, we calculate the matching label Y i-1 to make coarse semantic classification of visual samples, i.e., assign T i-1 to V. At the generative step, we first propose K text candidatesΦ prop (T i-1) for T i base on T i-1 to limit the decoding space. Then, to vote for the best candidate, we design two novel modules, namely inter-modal weighting Φ inter and intra-modal weighting Φ intra .Here, Φ inter uses vision information V, while Φ intra relies on text information T i-1 .We alternate between the generative and discriminative steps to optimize the decoding result step by step. Please find in Algorithm 1 for comprehensive details. EQUATIONRecall t c,i is the i-th word to be decoded, and T c,i-1 is from last decoding, with the first i -1 words decoded. As we decode word-by-word, choosing the best T c,i is exactly choosing the best t c,i ,i.e. argmax Tc,i p(T c,i |T c,i-1 , V c ) = argmax tc,i p(t c,i |T c,i-1 , V c), as we do in generative step.Step here consists in, for each semantic-class c, choosing the best t c,i that maximizesp(t c,i |T c,i-1 , V c ). With p(T c,i-1 , V c) and p(V c ) same for all possible t c,i , we make detailed derivations in the Appendix to show that:EQUATIONHere, the error model p(T c,i-1 |t c,i , v j ) evaluates how t c,i may be misspelled as t ′ c,i , since the i-th word in T c,i-1 is still noisy and not decoded. Knowing that errors in text descriptions are independent of visual samples, it reduces to uni-modal p(T c,i-1 |t c,i ). As the error that one may make given the correct text is harder to model while the reverse is much easier, we letp(T c,i-1 |t c,i ) ∝ p(t c,i |T c,i-1 ).Please refer to detailed derivations in the Appendix. As a result, our final objective is:EQUATIONText Proposals consists in proposing K candidates {t k i } k for t i with the lowest Levenshtein Edit Distance D(•, t ′ i ) (a metric of spelling similarity). By replacing original noisy wordt ′ i in T k i-1 with {t k i } k , they form Φ prop (T i-1 ) = T k i = (t 1 , • • • , t i-1 , t k i , t ′ i+1 , • • • , t ′ n ), the K candidates for T i .The benefit of text proposals is to reduce computing complexity. Since text embeddings are quantized in the semantic space, the search is limited to proposed candidates, rather than in the entire space.Inter-modal Weighting Φ inter = p(t c,i |v j ), v j ∈ V c relies on vision samples from semantic-class c to determine the best t c,i for the next iteration. Concretely, we model the probability of being chosen T 0 ← T ′ for i = 1, 2, • • • , n do for c = 1, 2, • • • , C do ▷ Text Proposals t ′ c,i is the i-th word of T c,i-1 , which is noisy and not yet decoded Select from corpus, K candidates {t k c,i } k with the smallest D with t ′ c,i Replace t ′ c,i with {t k c,i } k , forming {T k c,i } k end for for j = 1, 2, • • • , |V| do ▷ Discriminative Step c ← argmax c max k exp(S(vj ,T k c,i )) k ′ exp(S(vj ,T k ′ c,i )) Assign v j to class c, v j ∈ V c end for for c = 1, 2, • • • , C do ▷ Generative Step Φ k intra ← exp(-D(t k c,i ,t ′ c,i )/λ) k ′ exp(-D(t k ′ c,i ,t ′ c,i )/λ) ▷ Intra-Modal Weighting Φ k inter ← vj ∈Vc exp(S(vj ,T k c,i )) k ′ exp(S(vj ,T k ′ c,i )) ▷ Inter-Modal Weighting T c,i ← T k c,i , k = argmax k Φ k intra × Φ k inter end for end forfor each proposed candidate to be:EQUATIONwhere S(•, •) is the cosine similarity between video-text embeddings, both encoded by Φ OVAR . The intuition is that the more unanimously visual samples agree on candidate T k c,i , the more likely it is the text descriptions corresponding to semantic-class c. Besides, by letting visual samples vote on T k c,i instead of t k c,i , we take into consideration not only the current word t c,i but also context implicitly.Intra-modal Weighting Φ intra = p(t c,i |T c,i-1 ) relies solely on text information to decide the best t c,i for next iteration. Although Φ intra may be solved by uni-modal spell-checkers [15] or large language models [1], we here design a simple model by considering only spelling similarity (ignore contexts), to save computing costs. That is, choose t c,i depending solely on t ′ c,i instead of on entire T c,i-1 :EQUATION)The intuition is that, the more similar a word candidate t k c,i is, compared to the noisy word t ′ c,i , the more likely it is the corresponding denoised word. Here, we introduce one temperature parameter λ to balance Φ intra and Φ inter . A larger λ indicates that different edit distance gives similar probabilities, meaning that we rely more on visual samples for decision, and vice versa.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?Answer: [Yes] Justification: We provide source code. Datasets are publicly accessible.Guidelines:• The answer NA means that paper does not include experiments requiring code.• Please see the NeurIPS code and data submission guidelines (https://nips.cc/ public/guides/CodeSubmissionPolicy) for more details.• While we encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. 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If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.• At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).• Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 
experiments: 
 Typical Models for Vanilla OVAR. To illustrate the generalizability of our framework, we leverage two typical models from the VLA pipeline as Φ OVAR , that is, ActionCLIP [49] and XCLIP [62] .These two models adopt hand-crafted prompts and visual-conditioned prompt tuning, respectively.Under both models, we choose ViT-B/16-32F as the network backbones, for simplicity.Datasets. HMDB51 [26] contains 7k videos covering 51 action categories. UCF101 [45] contains 13k videos spanning 101 action categories. Kinetics700 [3] (K700) is simply an extension of K400, with around 650k video clips sourced from YouTube. To partition these datasets for open-vocabulary action recognition, this paper follows the standard consensus [49, 62] , for the sake of fairness. Text noises may be classified into 4 types: inserting, substituting, swapping, and deleting characters. [2] In terms of edit distance, based on TOEFL-Spell dataset [10] , most of the text noises have an edit distance = 1 compared to the clean version. Nevertheless, the distribution tends to be positively skewed towards larger noise. Implementations. We set the proposal number K = 10. Intra-modal weighting and inter-modal weighting are both used to determine the best candidate. Temperature λ follows a linear schedule from 0.01 to 1. We use the same corpus as in PySpellChecker, which contains 70317 English words, for text proposals. For typical OVAR methods [49, 62] , we choose the ViT-B/16-32F checkpoint pretrained on K400 [24] to evaluate their zero-shot robustness on HMDB51 [27] , UCF101 [46] and K700 [44] . Since K700 and K400 have overlapped categories, we exclude them when evaluating on K700. For UCF101, we use the separated lowercase text label. All ablation studies are conducted on UCF101 under 20% noise. For statistical significance, We do each simulation 10 times and report the mean and confidence interval of 95%. All experiments are done using a single RTX 3090.Real Noise. We adopt two large-scale corpora [11, 10] of misspellings to analyze noise type in text descriptions. As shown in Fig. 3 , the conclusion is similar to the NLP community [42, 47] , i.e., three atomic types of noise are inserting, substituting, and deleting text characters. More complicated noise patterns, e.g. swaping, can be constructed by mixing atomic noise types. Then, following previous literature, we quantify noise rate through Levenshtein Edit Distance, a generally accepted metric, to calculate the occurrence number of atomic noise types. Specifically, GitHub Typo Corpus [11] contains over 350k edits of typos from GitHub. The average noise rate (per sentence) is 3.3%.Nevertheless, the distribution is highly positively skewed (skewness = 2.9). For the worst 5% cases, the noise rate (per sentence) is larger than 9.4%. TOEFL-Spell Corpus [10] samples essays written by candidates from various language backgrounds in TOEFL ® iBT test. There are, on average, 6.9spelling mistakes per essay. For misspelled words, the noise rate (per word) is on average 16.0%. Noise Scenarios. In the "Simulated" noise type, we mix three atomic noises: insertion, substitution, and deletion. Concretely, for each character, we perturb it with probability p. For each perturbation, it will be insertion, substitution, and deletion with equal probability. To further ensure real-world generalizability, we ask GPT3.5 to give examples of perturbation according to real-world scenarios.We mix them into simulated noises. Noise rate p of the "Real" noise type is estimated with Eq. (3). method outperforms all competitors by large margins, which is impressive because our method is unsupervised without prior knowledge other than those contained in the OVAR model. Note that the output of GPT 3.5 tends to be unstable depending on prompts, which requires manual cleaning to remove irrelevant parts contained in the output, thus impeding real-world usage.Comparisons Across Datasets/Models. Tab. 2 compares Top-1 Acc to further reveal our solution is scalable/generalizable. Under various noise rates, our model is robust to achieve huge improvements.In terms of scalability across models, our method is not only applicable to hand-crafted prompts as in ActionCLIP but also to learnable visual-conditioned prompts as in XCLIP. Furthermore, we notice that, whenever XCLIP outperforms ActionCLIP, our method also yields a better result. A better visual encoder and well-tuned prompt may significantly increase our performance, showing that our method's upper limit could become higher, as the community continues to train better OVAR models.Inter-modal Weighting Φ inter & Intra-modal Weighting Φ intra . Tab. 3 shows that, both Φ inter and Φ intra contribute to denoising text descriptions and to improving the robustness of underlying Φ OVAR . In terms of Top-1 Acc and Semantic Similarity, Φ inter performs better than Φ intra , since Φ inter uses visual information as one direct optimization guideline to improve video recognition.While Φ intra performs better in terms of Label Acc, which focuses more on spelling correctness.Besides, Φ inter and Φ intra turn out to be complementary: visual information helps to understand noisy text descriptions; while textual information prevents the model from being misled by visual samples. We achieve the best performance when combining these two weightings. Temperature Schedule λ balances intra-modal weighting and inter-modal weighting. One larger λ indicates more reliance on inter-modal weighting. "Linear" means that λ augments from 0.01 to 1 linearly. Tab. 3 reports that it is beneficial to rely more on intra-modal at the beginning of decoding, and then gradually turn to inter-modal for more help. This indicates that, when text noises are high, Φ intra offers more help; when text noises are slight, Φ inter could help more.Noise Type. Fig. 4 Left reports our robustness under various noise types/rates. "Mixed" means that three noise types: "Substitute", "Insert", "Delete" are equally possible to appear. Our method shows remarkable resilience when texts are perturbed by inserting or substituting characters. Performance degradation is observed when texts are perturbed by deleting characters. It is reasonable, as deleting characters causes huge information loss, making the model difficult to recover clean text descriptions.Number of Candidates K. Fig. 4 Right shows as K increases, inter-modal weighting can reveal its full power, hence improving performance. Otherwise, if a good candidate is excluded from the proposal stage due to a small K, it can be selected by neither of the inter-or intra-modal weighting, thus decreasing performance. Moreover, the performance tends towards one plateau, showing a decreasing marginal contribution of more proposals to performance. Since a larger K means more computing costs for text encoding, we select K = 10 by default to make reasonable trade-offs. 
conclusion: 
 This paper investigates how noises in class-text descriptions negatively interference OVAR; and one novel framework DENOISER is proposed for solutions. By incorporating visual information during denoising, we clarify the ambiguity induced by short and context-lacking text descriptions; by iteratively refining the denoised output through one generative-discriminative process, we mitigate cascaded errors which may propagate from spell-checking models to outputs of OVAR model. We conduct extensive experiments to demonstrate the generalizability of DENOISER across multiple models and datasets, and also show our superiority over uni-modal spell-checking solutions.Limitations. 1) We focus more on spelling noises; while in the real world, text noises can be more complex, involving semantic ambiguity. Equipping DENOISER with large language models may be a feasible solution. 2) Using more text candidates or visual samples brings better results for DENOISER, but also costs more. There is a trade-off between performance and computational cost. 
experimental setting/details: 
 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: We specify all settings of experiments in the experiments section.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: [Yes]Justification: We report confidence intervals.Guidelines:• The answer NA means that the paper does not include experiments.• The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.• The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).• The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)• The assumptions made should be given (e.g., Normally distributed errors).• It should be clear whether the error bar is the standard deviation or the standard error of the mean.• It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.• For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).• If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text. 
experiments compute resources: 
 Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?Answer: [Yes]Justification: We report information of computer resources.Guidelines:• The answer NA means that the paper does not include experiments.• The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.• The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.• The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper). Guidelines:• The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.• 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). 
broader impacts: 
 Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?Answer: [Yes] Justification: Our model helps users better leverage the existing Open-Vocabulary models in a more robust way.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 does 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: [NA] Justification: The paper poses no such risks.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: All the assets are properly cited. License and terms of use are properly respected.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.• The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.• If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.• For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. 
 
 Appendix: A.1 Decoding Objective At each step i, the decoding objective to find argmax ti p(t i |T i-1 , V). Note that, p(T i-1 , V) is same for all possible t i . As a result, our objective is written as:EQUATIONAt the discriminative step, we choose the best set of V that helps decode t c,i for each semantic-class c. To understand why V c , the set of visual samples v j whose labels Y j are assigned to semantic-class c are those who help decode most efficiently, we first introduce a hidden discrete random variablez j ∼ Q j for each v j , indicating the index of class assignment. z j = c means that argmax Y j = c.Knowing that all visual samples are independent and using Jensen inequality:EQUATIONEQUATIONEQUATIONEQUATIONEquality is attained at Q j (z j ) ∝ p(t i , T i-1 , v j , z j ). Since zj Q j (z j ) = 1, to maximize the lower bound, we have:EQUATIONGiven class texts and visual samples, the best estimation is:P(z j = c|T i , v j ) =    1 c = argmax c max k exp(S(vj ,T k c,i )) k ′ exp(S(vj ,T k ′ c,i ))0 otherwise (22) Note that, Q j is well defined because:EQUATIONWith Q j defined in this way, we find the discriminative step to be identical to how Φ OVAR assigns labels. We have Q j (c) = 1 only for {j|v j ∈ V c }:EQUATION(28)We optimize t c,i for each semantic-class:EQUATION= argmaxEQUATIONNoting that p(T c,i-1 ) is the same for any possible t c,i :EQUATION= argmaxEQUATION= argmaxEQUATIONIt is possible to optimize with prior p(t c,i ) by considering that the more a word is frequent, the less it is likely to be misspelled in real-world scenarios. In this paper, for simplicity, we assume the t c,i to be uniform:EQUATIONB Additional Experiments Fig. 6 ablates on the number of visual samples in Φ inter . Our method shows a drop in performance when fewer visual samples are used in Φ inter . The performance tends to converge towards that when solely Φ intra is used. We hypothesize that fewer visual samples make Φ inter harder to extract added value to Φ intra . With the noise rate increasing, we find an increasingly large drop in performance, which shows conversely that Φ inter is more important under large noise scenarios as textual information becomes more ambiguous and less informative.Fig. 7 visualizes the embedding of (visual samples, text descriptions) from three semantic-classes: bird (green), ship (yellow), truck (blue) in CIFAR-10 using T-SNE. The first principal component of textual embedding is removed following ReCLIP [12] to prevent them from clustering at the same place. The Left shows that classification accuracy is low when text descriptions are noisy. Almost all visual samples are recognized as "bird". The Middle shows the embeddings of proposed text candidates. Some of them remain at the same place, because they move perpendicular to this 2D space in the real semantic space. We assign the best set of visual samples for each semantic-class to help denoise, e.g., the blue dots are used to vote on the two candidates "trump" (red) and "truck" (purple)of "trumk". The Right shows that the denoised text descriptions improve the OVAR performance.Tab. 4 quantifies some good/bad cases. We find GPT 3.5 is better at understanding semantics of noisy text descriptions, e.g., "wal4ingm with a dog" → "dogwalking". However, its output is highly Table 4 : Cases of Denoised Text Descriptions for GPT 3.5 and DENOISER. The output from GPT 3.5 [1] tends to be unstable, and sometimes it's a relatively high-level understanding of noisy text descriptions. Our DENOISER ensures a relatively faithful output in terms of spelling but could be slightly mistaken when two words are similar in terms of both semantics and spelling. affected by input prompts, and thus tends to be unstable: important text parts are sometimes omitted or misinterpreted, e.g., "babty crawling" → "baby crying". Such unstable outputs require manual cleaning, limiting its applications in reality. Our DENOISER remains faithful in terms of spelling, e.g., "wal4ingm with a dog" → "walking with a dog" instead of "dogwalking". While it may be mistaken when two words are similar in semantics and spelling (rare cases), e.g., "ball" and "balls".Our model requires a trade-off between computational cost and performance. As shown in Fig. 4 and Fig. 6 , the performance of our DENOISER increases as the number of proposals K and the percentage of the visual samples used. Since the theoretical complexity of DENOISER increases linearly with K and the percentage of visual samples used, while the marginal contribution of a larger K or percentage is decreasing, a trade-off between computational cost and performance is necessary. 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. 


Decision: This paper receives mixed reviews: three (borderline) rejects and one borderline accept. Some major concerns on the motivation of the proposed method, outdated baseline choice, and some unclear details. The AC agrees that the proposed technique should be more general with text denoising, which should not be only limited to open vocabulary action recognition. Therefore, the authors are encouraged to verify the effectiveness of proposed method in a more general setting. So, the AC thinks this paper is not ready for publication and makes a reject recommendation.

Reviews: review: paper_summary: paper_summary: This paper addresses the challenge of noisy text descriptions in  Open-Vocabulary Action Recognition. It introduces the DENOISER  framework, which combines generative and discriminative approaches to denoise the text descriptions and improve the accuracy of visual sample  classification. The paper provides empirical evidence of the framework's  robustness and conducts detailed analyses of its components.
strength_weakness: strength_weakness: 1. As the authors state in the limitations section, textual description noise does exist, but it can be corrected with an offline language model, what are the advantages of the authors' proposed approach?
2. I would assume that the text noise problem presented in this paper is even worse on large video datasets collected by semi-automatically labeled networks, e.g., Panda70M, howto100M, and InternVid. I suggest that the authors might consider validating their ideas on these datasets.
questions: questions: 1. Citation [49] Figure 3, reports ActionClip's zeroshot Top-1 accuracy performance for HMDB-51 and UCF-101, which is 50% and 70%, respectively. Why is it different from the baseline results in Table 2 in this paper?
2. Some contrastive learning pre-training methods for text noise have been proposed, e.g., [1-2], and I have not seen any relevant discussions or experimental comparisons in the papers.

[1] Karim, Nazmul, et al. "Unicon: Combating label noise through uniform selection and contrastive learning." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.
[2] Ghosh, Aritra, and Andrew Lan. "Contrastive learning improves model robustness under label noise." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
limitations: limitations: The authors have provided a limitations analysis in their paper and I have suggested some limitations in the Questions section.
paper_summary: paper_summary: This paper deals with the problem of Open-Vocabulary Action Recogniton (OVAR). Specifically, it focuses on the issue that the action labels provided by users may contain some noise such as misspellings and typos. The authors find that the existing OVAR methods' performance drops significantly in this situation.  Based on this analysis, they propose the DENOISER framework to reduce the noise in the action vocabulary.
strength_weakness: strength_weakness: 1. This paper actually focuses on text denoising and does not involve any specific action recognition technology. The author just chose the field of OVAR to verify the effectiveness of the proposed text-denoising method. The title is somewhat misleading. I think the author should regard text-denoising as the core contribution of the article instead of the so-called "robust OVAR"
2. The article focuses on too few and too simple types of text noise, including only single-letter deletions, insertions, or substitutions. These kinds of errors can be easily discovered and corrected through the editor's automatic spell check when users create a class vocabulary. This makes the method in this paper very limited in practical significance.
3. , The proposed method, although a somewhat complex theoretical derivation is carried out in the article, is very simple and intuitive: that is, for each word in the class label, selecting the one that can give the highest score to the sample classified into this category among several words that are closest to the word.  There is limited novelty or technical contribution.
questions: questions: See weakness.
limitations: limitations: The author states two limitations of the work in the paper.
paper_summary: paper_summary: This paper tackles the challenge of noisy text descriptions in Open-Vocabulary Action Recognition (OVAR), a task that associates videos with textual labels in computer vision. The authors identify the issue of text noise, such as typos and misspellings, which can hinder the performance of OVAR systems. To address this, they propose a novel framework named DENOISER, which consists of generative and discriminative components. The generative part corrects the noisy text, while the discriminative part matches visual samples with the cleaned text. The framework is optimized through alternating iterations between the two components, leading to improved recognition accuracy and noise reduction. Experiments show that DENOISER outperforms existing methods, confirming its effectiveness in enhancing OVAR robustness against textual noise.
strength_weakness: strength_weakness: - The baseline models are outdated and not tailored for OVAR. The authors failed to reference recent OVAR papers such as OpenVCLIP[1] (ICML 2023), FROSTER (ICLR 2024), and OTI (ACM MM 2023).

- In Table 1, it is evident that the proposed method outperforms GPT-3.5. Additionally, the authors present examples in Table 4 to demonstrate the superiority of the proposed method over GPT-3.5. However, upon personal experimentation with all the examples from Table 4 using the provided prompt from the paper (lines 243-245), I observed that the GPT-3.5 model successfully rectified all issues, including challenging cases where the proposed method fell short. As a result, I remain unconvinced by the findings.

This is the prompt given to GPT-3.5 model, and I hope other reviewers can also try it on their own:

The following words may contain spelling errors by deleting, inserting, and substituting letters. You are a corrector of spelling errors. Give only the answer without explication. What is the correct spelling of the action of  “cutting i aitnchen”.


[1] Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization.

[2] FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition.

[3] Orthogonal Temporal Interpolation for Zero-Shot Video Recognition.
questions: questions: Please refer to the weaknesses.
limitations: limitations: Yes, they addressed the limitations.
paper_summary: paper_summary: This paper proposes a denoising framework to alleviate the influence of noisy text descriptions on open-vocabulary action recognition in real scenarios. A comprehensive analysis of the noise rate/type in text description is provided and the robustness evaluation of existing OVAR methods is conducted. A DENOISER framework with generative-discriminative optimization is proposed. The experiments demonstrate the effectiveness of the framework.
strength_weakness: strength_weakness: - About the experiments,
    - The reviewer thinks that the most convincing results are the Top-1 Acc of existing OVAR models under the Real noise type. However, in Table 1, the proposed model does not demonstrate much superiority compared to GPT3.5's simple correction. The reviewer worries about the research significance of this problem. Will this problem be well resolved when using more powerful GPT4/GPT4o with some engineering prompt designs?
    - In Table 2, I would like to see the performance of other correction methods (e.g., GPT3.5/4/4o) for a more comprehensive comparison.
    - Since this work focuses on the noise text description problem in OVAR, it is necessary to demonstrate the results of those CLIP-based methods without any additional textual adaptation (e.g., the vanilla CLIP).


- About the method,
    - The reviewer thinks that the overall model design is reasonable and clear. However, the method part introduces too many symbols which makes the paper very hard to follow. It is unnecessary to over-decorate the technical contributions.

- Minor issue,
    - The authors seem to have a misunderstanding about the OVAR setting (Line 113). In OVAR, the model is evaluated on both base-classes and novel-classes during testing. In this case, all action classes from the UCF/HMDB datasets can be used for testing when the model is trained on K400, as there are many overlap classes between K400 and UCF/HMDB.
questions: questions: Please see the weaknesses section.
limitations: limitations: The limitations are discussed and there is no potential negative societal impact.

Comments: title: Response to Authors
Response to Comments
Response to Rebuttal
post-rebuttal-1
comment: Dear Reviewer YJ4Z,

We have responded to your comments in detail. As the discussion period will end in less than 5 hours, we would like to ask whether there are any additional concerns or questions we could address.

Thanks very much for your effort!

Best regards,

Authors
Thank you for sharing your code.

Using the API, I was able to successfully reproduce your results. Interestingly, when I tested the cases directly on the GPT-3.5 conversation webpage, I found that the model could resolve all the issues. This suggests there may be some underlying factors at play. Regardless, I am now convinced by the results.

After reviewing the feedback from Reviewer eAxG, I agree that this method has no specific designs for OVAR and should be tested on a broader range of tasks. A more comprehensive validation would undoubtedly make the paper more convincing.

Therefore, I believe the paper needs additional experiments across more benchmarks, such as image-based tasks, to be considered ready for a conference like NIPS. I appreciate the efforts the authors have put into the rebuttal and discussion. If they address the reviews and improve the paper accordingly, it will become a more compelling piece of work.

Considering all the discussion, I keep my score to be borderline reject. 

Thank you.
```python
import json
import http.client

conn = http.client.HTTPSConnection("api.openai.com")
payload = json.dumps({
"model": "gpt-3.5-turbo",
"messages": [
        {
            "role": "system",
            "content": 'The following words may contain spelling errors by deleting, inserting and substituting letters. You are a corrector of spelling errors. Give only the answer without explication.',
        },
        {
            "role": "user",
            "content": f'What is the correct spelling of the action of "writing on boarUd".'
        }
],
})
headers = {
'Accept': 'application/json',
'Authorization': 'Bearer your api key',
'Content-Type': 'application/json'
}
conn.request("POST", "/v1/chat/completions", payload, headers)
res = conn.getresponse().read().decode("utf-8")
res = json.loads(res)['choices'][0]['message']['content']
```
Thanks Reviewer bX9i for the timely reply.

We are pleased to see that our rebuttal has addressed all 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.

Since the reviewer feels that we have proposed an interesting and meaningful problem and that our contribution is valuable to the community. We invite the reviewer to kindly raise the recommendation rating. We can't let an interesting and worthwhile endeavor go unnoticed.
Thanks for your comment.

Could you please provide me the code that you query GPT-3.5 for correcting the issues? And, it would be better you provide the cases that you find GPT fails to solve while your method successfully corrects.
Thanks Reviewer 5bE7 for your timely reply.

We feel that we cannot agree with each other on the specific example of "cutting i aitnchen". Thus, we propose that we could try another one.

Here is the setting for OpenAI API:
- Model: "gpt-3.5-turbo"
- System Message: "The following words may contain spelling errors by deleting, inserting and substituting letters. You are a corrector of spelling errors. Give only the answer without explication."
- User Message: "What is the correct spelling of the action of "writing on boarUd"."

Here is the output of GPT:
- whiteboard
- board
- board
- whiteboarding
- board
- board
- board
- board
- board
- board
- board
- The correct spelling is "writing on board".
- board
- board
- board
- board
- whiteboarding
- board
- board
- "boarding"
- board
- board
- onboard

Furthermore, we believe that focusing on a single example may lead us to ignore the whole picture. Instead of focusing on a single example, we would like to present here how GPT and our method denoise the text descriptions in a quantitative way.

Experiments conducted under a noise rate of 5% on UCF101:
|        | Acc-0 | Acc-1 | Acc-2 | Mean Edit Distance |
|:------:|:-----:|:-----:|:-----:|:----------:|
| GPT3.5 | 47.63 | 71.49  |  73.77 |   2.59   |
|  Ours  | 65.69  |  80.70  | 91.75 |   0.67   |
where:
- Edit Distance : Levenshtein edit distance
- Acc-0: Denoised text description matches exactly the original text description
- Acc-1: Denoised text description is of edit distance<=1 from the original one
- Acc-2: Denoised text description is of edit distance<=2 from the original one
- Mean Edit Distance: mean edit distance between the denoised text description and the original one

GPT may recover on average 73% of the time, a denoised text who has an edit distance smaller than 2 with the original text, which is still consistent with the reviewer's experiment.

We are not trying to deny the power of GPT in such tasks. GPT is a respected competitor, yet not tailed for such tasks.

Our method surpasses GPT by a notable margin, without a complex language model.
Thanks to the authors for their efforts during the rebuttal. I have read all the comments from other reviewers and the authors' responses. I agree with Reviewer 5bE7 that GPT's performance in text correction may be underestimated and agree with Reviewer bX9i that the technical contribution of the proposed method is somewhat over-decorated. Besides, I stand by my opinion that this paper actually focuses on text denoising and does not involve any specific action recognition technology. The proposed text-denoising method should be evaluated on more text-related tasks beyond OVAR. The types of text noise that the paper focuses on are too few and too simple. For OVAR, considering that the largest action recognition dataset has only hundreds of categories, even if there is a 10% error rate, the errors can be easily discovered through the editor's automatic spell check and then corrected manually when users create a class vocabulary. Only in fields where the amount of text data is very large, the text denoising method proposed will have practical significance. I decide to keep my initial rating for the above reasons.
Thanks to the authors for their detailed rebuttal.

Regarding the first point, I recommend the authors to include additional experiments and references in the revision.

Regarding the second point, I tried the prompt provided by the authors with the GPT-3.5 model ten times, and I found the model can correct the spelling for every run. 

Below is the log:

1. The correct spelling is "cutting in the kitchen".
2. The correct spelling is "cutting in the kitchen."
3. The correct spelling is "cutting in the kitchen".
4. The correct spelling is "cutting in the kitchen".
5. The correct spelling is "cutting in a kitchen".
6. The correct spelling is "cutting in a kitchen."
7. The correct spelling is "cutting in a kitchen."
8. The correct spelling is "cutting in a kitchen".
9. The correct spelling is "cutting in the kitchen".
10. The correct spelling is "cutting in the kitchen."

Therefore, I am still not convinced by the rebuttal. 

After consideration, I decide to raise my score to 4.
Thanks to the authors for their efforts during the rebuttal. After carefully reading the responses, my concerns are well resolved. Considering the concerns from other reviewers, I decided to keep my rating at "borderline accept".
Dear Reviewer bX9i,

We hope this message finds you well. I am reaching out to kindly request your prompt response to confirm whether our responses adequately address your queries. We sincerely thank you for your time and effort during this discussion period. Your timely feedback is greatly appreciated.
Dear Reviewer 5bE7,

We hope this message finds you well. I am reaching out to kindly request your prompt response to confirm whether our responses adequately address your queries. We sincerely thank you for your time and effort during this discussion period. Your timely feedback is greatly appreciated.
Dear Reviewer eAxG,

We hope this message finds you well. I am reaching out to kindly request your prompt response to confirm whether our responses adequately address your queries. We sincerely thank you for your time and effort during this discussion period. Your timely feedback is greatly appreciated.
Dear Reviewer YJ4Z,

We hope this message finds you well. I am reaching out to kindly request your prompt response to confirm whether our responses adequately address your queries. We sincerely thank you for your time and effort during this discussion period. Your timely feedback is greatly appreciated.