Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding

2025-06-17 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Active Video Perception (AVP) is an evidence-seeking framework designed to overcome the challenges of long video understanding (LVU), where relevant cues are sparse and dispersed within extensive, often redundant, content. Unlike traditional agentic pipelines that rely on query-agnostic captioning, AVP treats video as an interactive environment, actively deciding what, when, and where to observe. It employs an iterative plan–observe–reflect process using MLLM agents: a planner proposes targeted interactions, an observer extracts time-stamped evidence, and a reflector assesses evidence sufficiency to either answer or trigger further observation. AVP achieved the highest performance across five LVU benchmarks, including MINERVA and LVBench. Notably, it surpassed the leading agentic method, DeepVideoDiscovery (DVD), by 5.7% in average accuracy, while reducing inference time by 81.6% (to 18.4% of DVD's) and input tokens by 87.6% (to 12.4% of DVD's).

Key takeaway

For machine learning engineers developing long video understanding solutions, traditional passive captioning methods are computationally expensive and often lack precision. You should consider adopting an active perception framework like AVP, which iteratively plans, observes, and reflects on query-relevant video segments. This approach significantly improves accuracy and efficiency, reducing inference time and token usage compared to prior agentic methods. Implement query-driven observation to focus computation on critical evidence.

Key insights

Long video understanding agents should actively seek query-relevant evidence through iterative, goal-directed observation.

Principles

• Active perception significantly boosts long video understanding efficiency and accuracy.
• Iterative plan-observe-reflect cycles enable adaptive, refined evidence gathering.
• Query-driven observation focuses computation, avoiding irrelevant content processing.

Method

AVP employs MLLM agents in an iterative plan–observe–reflect loop: a planner proposes targeted observation (what, where, how), an observer extracts structured time-stamped evidence, and a reflector assesses sufficiency, guiding replanning or halting.

In practice

• Decompose complex queries for multi-step reasoning and evidence seeking.
• Start with coarse video scans, then refine observation to specific regions.
• Organize extracted evidence into structured, time-aligned lists.

Topics

• Active Video Perception
• Long Video Understanding
• MLLM Agents
• Iterative Reasoning
• Video Question Answering
• Computational Efficiency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.