Hugging Face Trending Papers
Identity-Conditioned Score Fusion for Open-Set Person Re-Identification
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arXiv Machine Learning
Oct 7
Identity-Conditioned Score Fusion for Open-Set Person Re-Identification
The paper proposes identity-conditioned score fusion for open-set person re-identification, a framework that assigns weights tailored to each gallery identity without requiring additional training. By comparing intra-identity consistency with cross-identity impostors, it derives identity-specific profiles that work with query-conditioned adaptation through a parameter-free rule, enhancing the separation between true and false matches while maintaining score calibration. Experiments on three clothes-changing benchmarks show consistent improvements over statistical, rank-based, and learned baselines, achieving up to an 8.8% absolute reduction in the false non-identification rate.
Hugging Face Trending Papers
Oct 20
MINER: Modeling Inter- and Intra-Person Variance for Reidentification
Clothes-Changing Person Re-Identification (CC-ReID) remains challenging due to drastic appearance shifts across outfits. Most prior works devise biometric signal-extraction heuristics to enhance inter...
arXiv Computer Vision
Oct 22
MINER: Modeling Inter- and Intra-Person Variance for Reidentification
MINER is a model-agnostic framework designed to improve clothes-changing person re-identification by suppressing garment bias. It works by contrasting hard examples and adaptively balancing intra- and inter-person variance in the dataset. Experiments demonstrate that MINER achieves state-of-the-art performance on four CC-ReID benchmarks and ranks among the leaders in all three tracks of the open TVRID RGB-Depth ReID challenge.
arXiv Computer Vision
Oct 27
UniQuery: Unifying Vision Models with a Joint Query Head for Cross-Domain Pedestrian Attribute Recognition
arXiv:2610.33639v1 Announce Type: new Abstract: Pedestrian Attribute Recognition (PAR) predicts semantic attributes of individuals, such as gender, age, clothing and accessories, and its descriptions...
arXiv Machine Learning
Jun 8
Does Appearance Help? A Systematic Study of Image-Based Re-Identification in Online 3D Multi-Pedestrian Tracking
arXiv:2606. 07233v1 Announce Type: cross Abstract: LiDAR-based 3D Multi-Object Tracking (MOT) typically relies solely on geometric information, which is often insufficient to distinguish between targets during prolonged occlusions or in crowded human-populated environments.
arXiv AI
Jun 2
Understanding Identity Continuity in Thermal Video through Scene-Level Consistency
arXiv:2606. 01694v1 Announce Type: cross Abstract: Thermal pedestrian MOT remains challenging because weak appearance cues and frequent detection interruptions cause severe trajectory fragmentation.