By adding identity-aware sampling and a contrastive loss on a new 28-dataset benchmark, the authors build multimodal embeddings that are far better at visual identity matching without losing general retrieval accuracy.
Deep learning for person re- identification: A survey and outlook.IEEE transactions on pattern analysis and machine intelligence, 44(6):2872–2893,
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Illuminating Visual Identity in Universal Multimodal Embeddings
By adding identity-aware sampling and a contrastive loss on a new 28-dataset benchmark, the authors build multimodal embeddings that are far better at visual identity matching without losing general retrieval accuracy.