This paper introduces Agent-of-Thoughts Distillation (AoTD), a method that enhances Video-LLMs by incorporating automatically generated Chain-of-Thoughts into instruction-tuning. AoTD leverages specialized agent models to break down complex video questions into simpler sub-tasks, generating high-quality reasoning chains.
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https://arxiv.org/abs/2412.01694
🤔 Original Problem:
→ Current Video-LLMs lack explainability and struggle with spatial-temporal grounding, making them unreliable for real-world applications where transparency is crucial.
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🛠️ Solution in this Paper:
→ AoTD decomposes complex video questions into manageable sub-tasks using an agent-based system.
→ Specialized vision models handle each sub-task sequentially, with their outputs forming reasoning chains.
→ A verification mechanism using LLMs ensures reliability of generated Chain-of-Thoughts.
→ The filtered high-quality reasoning chains are distilled into Video-LLMs through instruction-tuning.
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💡 Key Insights:
→ Multi-step reasoning improves both performance and interpretability in video understanding
→ Agent-based systems can effectively generate reasoning chains automatically
→ LLM verification significantly enhances the quality of distilled knowledge
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📊 Results:
→ Outperformed existing methods on multiple-choice and open-ended VideoQA benchmarks
→ Achieved 74.3% accuracy on STAR dataset, surpassing previous state-of-the-art
→ Improved spatial-temporal grounding with 24.7% IoU and 35.3% Recall
First Set:
Teaching Video-LLMs to think step-by-step using specialized agents as tutors
Video-LLMs learn complex reasoning by watching expert agents solve problems
Breaking down video questions into bite-sized pieces for smarter AI understanding
Making Video-LLMs explain their thinking like a chain of expert consultants
Second Set:
Video AI now learns to solve problems like a detective team - one clue at a time
Teaching robots to watch videos and explain their thoughts like a smart friend
Making AI break down video puzzles into simple steps, just like humans do
Video AI gets smarter by learning from a team of expert mini-AIs
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