Generative Semantic Multi-Object Tracking: A Large-Scale Benchmark and an MLLM-Driven Reasoning Framework
arXiv:2601.06550v3 Announce Type: replace-cross Abstract: Semantic Multi-Object Tracking (SMOT) is evolving from purely geometric localization toward comprehensive video understanding. However, existing paradigms predominantly rely on closed-set interaction tags and fragmented perception pipelines, creating a bottleneck that prevents the full utilization of Multi-modal Large Language Models (MLLMs) for dynamic scenes. In this paper, we elevate SMOT from rigid classification to an open-ended generative reasoning task. To support this paradigm shift, we introduce Grand-SMOT, a large-scale benchm
The proliferation of Multi-modal Large Language Models (MLLMs) is enabling a shift from rigid, classification-based video understanding to more open-ended generative reasoning, pushing the boundaries of existing object tracking paradigms.
This development indicates a significant advancement in AI's ability to understand dynamic visual information, expanding beyond simple object identification to interpret complex scenes and interactions, which is critical for autonomous systems and AI agents.
Semantic Multi-Object Tracking (SMOT) evolves from primarily geometric localization and closed-set interaction tags to an open-ended generative reasoning task, supported by new large-scale benchmarks tailored for MLLMs.
- · AI/ML researchers
- · Robotics industry
- · Autonomous vehicle developers
- · Generative AI platforms
- · Companies reliant on closed-set vision systems
- · Traditional video analytics providers
Improved situational awareness and decision-making for autonomous systems in complex, dynamic environments.
Acceleration of AI agent development, allowing them to better perceive and interact with the physical world.
Enhanced human-robot collaboration through more sophisticated scene understanding and predictive capabilities.
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Read at arXiv cs.AI