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[2602.13313] Agentic Spatio-Temporal Grounding via Collaborative Reasoning
Summary
The paper presents the Agentic Spatio-Temporal Grounder (ASTG), a novel framework for Spatio-Temporal Video Grounding (STVG) that enhances retrieval efficiency by using collaborative reasoning agents, outperforming existing methods in weakly-supervised and zero-shot scenarios.
Why It Matters
This research addresses significant challenges in video grounding, such as high supervision requirements and limited generalization. By proposing a training-free approach, it opens new avenues for efficient video analysis, which is crucial for applications in AI-driven video content understanding and retrieval.
Key Takeaways
- ASTG framework utilizes two specialized agents for enhanced video grounding.
- Decouples spatio-temporal reasoning to automate extraction and verification.
- Outperforms existing weakly-supervised and zero-shot methods.
- Demonstrates comparable performance to fully-supervised approaches.
- Addresses the limitations of traditional frame-wise localization methods.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.13313 (cs) [Submitted on 10 Feb 2026] Title:Agentic Spatio-Temporal Grounding via Collaborative Reasoning Authors:Heng Zhao, Yew-Soon Ong, Joey Tianyi Zhou View a PDF of the paper titled Agentic Spatio-Temporal Grounding via Collaborative Reasoning, by Heng Zhao and 2 other authors View PDF HTML (experimental) Abstract:Spatio-Temporal Video Grounding (STVG) aims to retrieve the spatio-temporal tube of a target object or person in a video given a text query. Most existing approaches perform frame-wise spatial localization within a predicted temporal span, resulting in redundant computation, heavy supervision requirements, and limited generalization. Weakly-supervised variants mitigate annotation costs but remain constrained by the dataset-level train-and-fit paradigm with an inferior performance. To address these challenges, we propose the Agentic Spatio-Temporal Grounder (ASTG) framework for the task of STVG towards an open-world and training-free scenario. Specifically, two specialized agents SRA (Spatial Reasoning Agent) and TRA (Temporal Reasoning Agent) constructed leveraging on modern Multimoal Large Language Models (MLLMs) work collaboratively to retrieve the target tube in an autonomous and self-guided manner. Following a propose-and-evaluation paradigm, ASTG duly decouples spatio-temporal reasoning and automates the tube extraction, verification and temporal localization processes. With a dedicat...