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arXiv Computer Vision By Tahar Chettaoui, Guray Ozgur, Eduarda Caldeira, Naser Damer, Fadi Boutros

Hard Sample-Conditioned Decorrelated Attention for Robust Face Recognition

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The paper introduces ViT‑HarDis, a method that conditions attention head disentanglement on sample difficulty in Vision Transformers for face recognition. By applying stronger head decorrelation to hard samples—those with high cosine similarity to negative class centers—the approach encourages specialization where identity ambiguity is greatest. Experiments on WebFace4M and MS1MV3 show that ViT‑HarDis improves over baseline and uniform disentanglement, achieving state‑of‑the‑art results on IJB‑B, IJB‑C, and TinyFace without architectural changes or extra inference cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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