Venue: NeurIPS 2022 Workshop TSRML

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: summary: This paper introduces a novel dataset, FAX, to evaluate the face attribute diversity in unlabeled datasets. In addition, the authors propose a conditional model that learns to predict human judgments of face similarity. They demonstrate that an annotator’s demographics influences the importance they place on different attributes.
strengths: - This paper introduces a new public dataset of odd-one-out face similarity judgments. This is important since odd-one-out judgments implicitly encode the dimensions relevant to pairwise similarity which is easier than explicit pairwise similarity.
- The proposed method is evaluated on various face recognition models and datasets.

weaknesses: - The paper's writing requires further improvement. It is difficult to follow certain sections of the paper. In addition, the conclusion should contain a concise summary of the paper's key points.
- The authors only evaluate their models using ResNet18 backbones and 128-dimensional embeddings.

overall_recommendation: This paper introduces a new dataset of human judgments of face similarity which will be beneficial to the community. This paper requires additional evaluations and improved writing.
review_title: A good paper introducing a novel dataset of human judgments of face similarity

Decision: Accept