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.
By Oron Nir, Eliyahu Strugo, Ariel Shamir
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.
By Dong-Woo Kim, Tae-Kyun Kim
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.
By Manyi Yao, Jurijs Nazarovs, Eunji Chong, Abhishek Sharma, Rohan Sarkar, Yue Guo, Christian R. Shelton, Amit K. Roy-Chowdhury, Debashish Pal
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...
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.
By Jie Wang, Qian He, Gaofeng He, Xiaogang Jin, Huamin Wang
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...
By Nguyen Dang Duy Tan, Linh T. T. Tran, Huynh Nguyen Phat, Vo Thanh Nguyen
The paper introduces PRAISE, a test‑time adaptation framework for vision‑language model based person re‑identification. PRAISE constructs an Attribute‑Conditioned Subspace (ACS) from a bank of attribute prompts, reshaping query‑gallery affinities without requiring identity or attribute labels. Experiments on image‑to‑image and text‑to‑image ReID benchmarks show that PRAISE consistently improves retrieval performance over existing backbones and TTA baselines.
By Rajarshi Bhattacharya, Aryan Shukla, Shakeeb Murtaza, Christian Desrosiers, Jose Dolz, Maguelonne Heritier, Eric Granger
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.
By Eduardo Borges, Lu\'is Garrote, Urbano J. Nunes
The paper introduces a new framework for unsupervised visible‑infrared person re‑identification that leverages modality‑unified prototypes. By contrasting with prototypes that unify both modalities, the method jointly optimizes similarity within and across modalities, improving modality invariance. A self‑distillation step refines instance‑prototype relationships using a steady teacher, resulting in a simple yet effective model validated on standard VI‑ReID benchmarks.
By Menglin Wang, Xiaojin Gong
arXiv:2609.12577v1 Announce Type: new
Abstract: Lifelong Person Re-Identification (LReID) aims to train a unified person retrieval model from a non-stationary data stream. Existing LReID methods main...
By Kunlun Xu, Liangyu Ma, Jiangmeng Li, Xin Tong, Xiaode Liu, Yufei Guo, Jiahuan Zhou
arXiv:2609.36560v1 Announce Type: cross
Abstract: Object re-identification (ReID) faces a recurring challenge: different identities can share highly similar global appearances, while the cues that di...
By Zhiqi Li, Xiaowei Zhou, Zeyuan Sun, Feng Gao, Junyu Dong
arXiv:2606. 02242v1 Announce Type: cross Abstract: The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting training objectives, leading to suboptimal shared representations.
By Karina Kvanchiani, Timur Mamedov