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Found 6 papers, 5 papers with code
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
To further capture human characteristics, we propose a structure-invariant alignment loss that enforces different masked views, guided by the human part prior, to be closely aligned for the same image.
Unified Pre-training with Pseudo Texts for Text-To-Image Person Re-identification
In this way, the pre-training task and the T2I-ReID task are made consistent with each other on both data and training levels.
Mining False Positive Examples for Text-Based Person Re-identification
Text-based person re-identification (ReID) aims to identify images of the targeted person from a large-scale person image database according to a given textual description.
Learning Granularity-Unified Representations for Text-to-Image Person Re-identification
In PGU, we adopt a set of shared and learnable prototypes as the queries to extract diverse and semantically aligned features for both modalities in the granularity-unified feature space, which further promotes the ReID performance.
Quality-aware Part Models for Occluded Person Re-identification
Existing approaches typically rely on outside tools to infer visible body parts, which may be suboptimal in terms of both computational efficiency and ReID accuracy.
Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification
Third, we introduce a Compound Ranking (CR) loss that makes use of textual descriptions for other images of the same identity to provide extra supervision, thereby effectively reducing the intra-class variance in textual features.
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