A multi-view semantic reformulation and feature compensation method using LLMs and VLMs improves text-to-image person retrieval accuracy without training and reaches SOTA on three datasets.
Looking alike from far to near: Enhancing cross- resolution re-identification via feature vector panning
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Towards Robust Text-to-Image Person Retrieval: Multi-View Reformulation for Semantic Compensation
A multi-view semantic reformulation and feature compensation method using LLMs and VLMs improves text-to-image person retrieval accuracy without training and reaches SOTA on three datasets.