AI RESEARCH
Training-Free Semantic Multi-Object Tracking with Vision-Language Models
arXiv CS.CV
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ArXi:2604.14074v1 Announce Type: new Semantic Multi-Object Tracking (SMOT) extends multi-object tracking with semantic outputs such as video summaries, instance-level captions, and interaction labels, aiming to move from trajectories to human-interpretable descriptions of dynamic scenes. Existing SMOT systems are trained end-to-end, coupling progress to expensive supervision, limiting the ability to rapidly adapt to new foundation models and new interactions. We propose TF-SMOT, a