arXiv AI
FM-ReID: Selective Competitive Token Routing for Object Re-Identification
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FM-ReID: Selective Competitive Token Routing for Object Re-Identification
Object re-identification (ReID) faces a recurring challenge: different identities can share highly similar global appearances, while the cues that distinguish them are localized, heterogeneous, and vi...
arXiv AI
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VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects
arXiv:2607. 17157v1 Announce Type: cross Abstract: Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time.
arXiv AI
Sep 15
A Generative AI Integrated Multimodal Framework for Low-Latency Multi-Camera Person Re-Identification
arXiv:2609.14419v1 Announce Type: cross Abstract: Person re-identification (ReID) is essential for multi-camera surveillance and tracking, yet remains difficult due to viewpoint and illumination chan...
arXiv Machine Learning
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Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era
arXiv:2607. 22068v1 Announce Type: cross Abstract: Multi-branch architectures and CNN-Transformer fusion have long been regarded as effective ways to improve vehicle re-identification (Re-ID) by combining complementary representations.
arXiv Computer Vision
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Degradation-based augmented training for robust individual animal re-identification
arXiv:2603.04163v2 Announce Type: replace Abstract: Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based...
arXiv Computer Vision
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MINER: Multi-crop INference-time Enhancement for Rare-Object Retrieval with Frozen Dual Encoders
MINER is a training‑free inference framework that enhances frozen dual‑encoder models for text‑to‑image retrieval when queries refer to small, visually subordinate objects in cluttered scenes. It augments the global image embedding with a bank of region‑level embeddings and applies hubness‑correcting similarity rescoring, thereby recovering visual evidence that global pooling underweights. The authors introduce ROCS, a benchmark derived from Flickr30K and MS COCO, and demonstrate that MINER improves retrieval performance across CLIP, SigLIP, and SigLIP 2 backbones on both ROCS and standard splits, attributing gains mainly to broader spatial coverage rather than precise crop placement.