AI

Reviso: Not All Visual Evidence Deserves Equal Attention in Video Captioning

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 03/11/2026, 08:18:24 UTC.

Briefings

Reviso is an agentic video captioning framework designed to prioritize visual evidence over minor details, addressing the issue where MLLMs often overlook essential context. The system learns an observation strategy using a hierarchical Context--Focus--Points (CFP) plan, enabling goal-directed active perception that revisits temporal segments and combines tools for fine-grained inspection. Training involves a verification-guided pipeline with Group Relative Policy Optimization (GRPO) and key-fact rewards, demonstrating superior performance on four benchmarks compared to open-source baselines.

Key points

• Reviso uses a hierarchical Context--Focus--Points (CFP) observation plan to identify video context, prioritize key aspects, and define concrete observation targets.
• The framework employs Group Relative Policy Optimization (GRPO) with a key-fact reward to learn the optimal observation strategy for video captioning.

Context

Video captioning often suffers from MLLMs overemphasizing minor details while missing essential context, prompting the need for priority-guided active observation methods like Reviso.

Limitations

Evidence covers four video captioning benchmarks; specific performance metrics on these benchmarks are not detailed in the provided text.

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