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VIDEO-SKILL-COT: AI Learns Video Smarts by Studying Specific Skills

A new artificial intelligence framework called VIDEO-SKILL-COT (VIDEO-SKOT) is designed to help AI systems better understand videos by focusing on specific reasoning skills. This approach addresses a common problem where existing AI models struggle to adapt to the diverse demands of different video content, from movies to surveillance footage.

Traditional “Chain-of-Thought” (CoT) reasoning, where AI explains its thought process, often falls short because it uses general reasoning steps that aren’t tailored to the unique skills needed for each video domain. VIDEO-SKOT overcomes this by automatically identifying and leveraging “skill-aware” CoT supervisions, resulting in improved domain adaptation.

Here’s how it works:

  1. Skill Identification and Clustering: VIDEO-SKOT first analyzes training questions related to videos and identifies the specific reasoning skills required to answer them. For example, a question about a movie scene might require “inferring emotional tone from facial expressions” or “identifying thematic parallels between actions and overarching narrative themes.” These skills are then grouped into a taxonomy.

  2. Skill-Based CoT Annotation: Each training question is annotated with its top relevant skills. The AI then generates a multi-step CoT explanation tailored to those skills. Imagine a question about a room in a video. Instead of a generic explanation, the AI might break it down:
    • “Determine object location relative to a person’s orientation.”
    • “Assess spatial proximity of objects relative to a reference point.”
    • “Determine room boundaries using structural elements like walls and floors.”
  3. Skill-Specific Expert Learning: The system trains specialized “expert” modules, each focusing on a subset of reasoning skills. This is accomplished by assigning each input to the expert most closely aligned with the required reasoning skills. These modules are then fine-tuned using the skill-based CoT annotations.

The researchers evaluated VIDEO-SKOT on three different video understanding benchmarks:

  • E.T.-Bench: Tests temporal understanding (e.g., event sequencing).
  • VSI-Bench: Focuses on spatial understanding (e.g., object relationships).
  • CinePile: Assesses narrative understanding in movies.

Across all three benchmarks, VIDEO-SKOT consistently outperformed strong baseline models. For example, it improved performance on E.T.-Bench by +4.10 over LLaVA-Video, a state-of-the-art model. The results demonstrate that the modular, expert-driven framework significantly enhances domain adaptation.

The researchers also compared the reasoning process of VIDEO-SKOT with a baseline system that relies on generic chain of thought. In one example, VIDEO-SKOT started by identifiying the relevant skills (e.g. spatial proximity) and then breaking the task down into focus sub-questions, like comparing the location of a washer and refridgerator.

In conclusion, VIDEO-SKILL-COT offers a promising approach to improving video understanding in AI by focusing on skill-specific reasoning and enabling more effective adaptation to diverse video content.