一篇论文提出VLA-ReID方法，专门解决蜜蜂等高度相似物体跟踪中的身份切换问题，不用额外标注就把重识别精度拉高了一大截。
VLA-ReID将重识别重新建模为视频级关联任务，使用聚合历史轨迹特征作为查询、当前帧检测作为候选，直接优化全局关联。论文提出Frame-Common Appearance Estimation (FCAE)和Common-Appearance Suppression (CAS)模块，无需额外标注即可放大高度相似物体间的判别差异。在BEE24数据集上，VLA-ReID相比现有最优跟踪器提升HOTA 1.1、MOTA 0.3、AssR 2.6、AssA 0.7、IDF1 0.8，并减少28%的身份切换。实验验证了视频级重识别建模在MOT中的有效性。
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 among visually similar objects. Existing approaches also often require substantial additional annotations to enhance appearance discrimination. We propose Video-Level Association re-ID (VLA-ReID), which reformulates re-ID as video-level association modeling. It uses aggregated historical trajectory features as queries and all current-frame detections as candidates, enabling direct optimization of their global association at each frame. In addition, Frame-Common Appearance Estimation (FCAE) estimates a common appearance direction from current-frame detections, while Common-Appearance Suppression (CAS) removes the corresponding component along this direction from trajectory and detection features. This amplifies discriminative differences among highly similar objects without additional annotations. Experiments on BEE24 show that VLA-ReID improves HOTA by 1.1, MOTA by 0.3, AssR by 2.6, AssA by 0.7, and IDF1 by 0.8 over state-of-the-art trackers, while reducing identity switches by 28%. These results demonstrate the effectiveness of video-level re-ID modeling for appearance-based association in MOT.