Hugging Face Trending Papers
MINER: Modeling Inter- and Intra-Person Variance for Reidentification
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
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 Machine Learning
Jun 11
Learning Instance-Adaptive Low-Rank Orthogonal Subspaces for Clothes-Changing Person Re-Identification
arXiv:2606. 11661v1 Announce Type: cross Abstract: Clothes-changing person re-identification (CC-ReID) aims to recognize individuals despite drastic appearance changes caused by clothing variation.
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 5
Identity-Conditioned Score Fusion for Open-Set Person Re-Identification
Robust person re-identification often combines complementary cues such as face, gait, and body shape. While adaptive fusion typically targets query quality, model strength also varies across identitie...
arXiv Computer Vision
Oct 21
TAMF-VTON: Token-Wise Sparse Adaptation for Mask-Free Virtual Try-On
TAMF-VTON is a diffusion-based virtual try‑on system that eliminates the need for segmentation masks, parsing maps, pose representations, or spatial controls during inference. It uses token‑wise sparse Mixture‑of‑Experts adaptation to provide input‑dependent capacity, along with timestep‑weighted frequency supervision and adaptive offline data construction to improve high‑frequency reconstruction and cross‑category training. The method achieves top performance on single‑garment VITON‑HD and DressCode benchmarks, supports multi‑garment transfer, and can generate 1K‑resolution images in under 15 seconds on an RTX 4090.
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...