PONE-D-25-22333Zero-Shot Image Classification Based on Class Representation Learning and Attribute Embedding LearningPLOS ONE

Dear Dr. Li,

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Academic Editor

PLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Overall Evaluation:

This paper proposes a method named CRAE (Class Representation and Attribute Embedding) to address the limitation that existing approaches often fail to capture significant intra-attribute visual variations. The method introduces an attribute-based adaptive softmax activation to dynamically normalize attribute feature maps and further incorporates attribute-level contrastive learning and class representation learning with attribute embedding learning.

Comments:

1. Figure 1 is missing from the main manuscript, which makes it difficult to fully understand the method’s pipeline.

2. The attribute-level contrastive embedding bears strong similarity to prior work [1], but the paper does not clearly differentiate its approach or provide a comparative discussion.

3. Although the adaptive softmax activation is claimed as a key contribution, the paper lacks a dedicated section or sufficient analysis highlighting this component.

4. The baseline methods used for comparison are outdated. Including more recent state-of-the-art approaches would strengthen the experimental validation.

[1] Semantic Contrastive Embedding for Generalized Zero-Shot Learning. IJCV, 2022.

Reviewer #2: The manuscript introduces a well-designed and effective zero-shot learning method named CRAE, which jointly integrates class representation learning and attributes embedding learning to address challenges in conventional and generalized zero-shot image classification. The paper is well-structured and clearly written. The proposed approach is technically sound and demonstrates competitive performance on multiple datasets showing improvements over the comparable methods.

The following suggestions may further improve the quality and clarity of the paper:

1. While the combination of adaptive softmax and dual contrastive learning is compelling, a more in-depth theoretical explanation or deep analysis would be also necessary.

2. The choice of hard positive/negative thresholds (μ and ε) is important but appears empirically chosen. It should discuss the rationale and potential to generalize or learn these thresholds adaptively.

3. The ablation study would benefit from more visual evidence (e.g., t-SNE plots) comparing feature distributions.

4. May, it would be better to give a brief discussion on potential challenges (e.g., missing attributes, noisy labels, class imbalances) and future directions.

5. Some equations (e.g., Eq. 5) are complex and could be reformatted for readability.

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Reviewer #1: No

Reviewer #2: No

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Zero-Shot Image Classification Based on Class Representation Learning and Attribute Embedding Learning

PONE-D-25-22333R1

Dear Dr. Li,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Haofeng Zhang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Both reviewers suggested to accept this paper.

Reviewer #1:

Reviewer #2:

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: (No Response)

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors took the reviews seriously and answered in a satisfactory way all questions and requests. As such, it is my opinion that the paper should be accepted as is.

Reviewer #2: As Reviewer #2, I have reviewed the revised manuscript (PONE-D-25-22333R1) and your responses to my initial comments. Overall, I am pleased with the improvements.

The revisions have improved scientific rigor and clarity. However, consider adding baseline t-SNE plots (without CRAE) for comparison and exploring adaptive thresholds in future work. With these enhancements, the manuscript is now suitable for publication. Well done!

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

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