VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects
Multi-object tracking (MOT) aims to localize multiple objects in videos while preserving their identities over time. Long-term identity preservation remains difficult when objects are small, densely distributed, and highly similar in appearance, as in bee swarm scenes. Existing trackers rely on re-identification (re-ID) models trained through single-instance assignment (instance-level querying). At inference, however, MOT requires global assignment between multiple trajectories and detections, corresponding to video-level querying. This training-inference mismatch can cause identity switches a
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “VLA-ReID: Video-Level Association for Re-Identification in M” ≈ “Developer-Y/cs-video-courses””
- LinkedLinked via arxiv author · 85%Yanrong Qin →
“VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects”
- LinkedLinked via arxiv author · 85%Xiaoyan Cao →
“VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects”
- LinkedLinked via arxiv author · 85%Yao Yao →
“VLA-ReID: Video-Level Association for Re-Identification in Multi-Object Tracking with Highly Similar Objects”