Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition (2501.03230v1)

Abstract: Existing research of video understanding still struggles to achieve in-depth comprehension and reasoning in complex videos, primarily due to the under-exploration of two key bottlenecks: fine-grained spatial-temporal perceptive understanding and cognitive-level video scene comprehension. This paper bridges the gap by presenting a novel solution. We first introduce a novel video Multimodal LLM (MLLM), MotionEpic, which achieves fine-grained pixel-level spatial-temporal video grounding by integrating video spatial-temporal scene graph (STSG) representation. Building upon MotionEpic, we then develop a Video-of-Thought (VoT) reasoning framework. VoT inherits the Chain-of-Thought (CoT) core, breaking down a complex task into simpler and manageable sub-problems, and addressing them step-by-step from a low-level pixel perception to high-level cognitive interpretation. Extensive experiments across various complex video QA benchmarks demonstrate that our overall framework strikingly boosts existing state-of-the-art. To our knowledge, this is the first attempt at successfully implementing the CoT technique for achieving human-level video reasoning, where we show great potential in extending it to a wider range of video understanding scenarios. Project is open at https://haofei.vip/VoT

• The paper introduces MotionEpic, a model integrating STSG for precise pixel-level video grounding.
• The Video-of-Thought framework decomposes video understanding into sequential steps for enhanced multi-stage reasoning.
• Empirical evaluations show significant improvements in both zero-shot and fine-tuned benchmarks, underscoring its robust performance.

An Examination of "Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition"

The paper "Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition," presents a novel framework aimed at enhancing video understanding and reasoning by leveraging a structured approach. This research addresses the challenge of achieving comprehensive video understanding, which entails both accurate, fine-grained spatial-temporal perception and high-level cognitive video scene comprehension. The authors introduce MotionEpic, a Multimodal LLM (MLLM) that incorporates a spatial-temporal scene graph (STSG) representation for pixel-level video grounding. Furthermore, they present the Video-of-Thought (VoT) framework that adopts the Chain-of-Thought (CoT) methodology to break complex tasks into manageable sub-tasks, facilitating step-by-step reasoning from perception to cognitive interpretation.

Core Contributions

1. MotionEpic Model:
   • Integration of STSG: MotionEpic advances the field by integrating STSG representations into the video understanding process. This allows for fine-grained, pixel-level grounding of video data that aids in the precise perception of video content.
   • Architecture: The model utilizes a combination of the ViT-L/14 encoder, Q-Former projector, and a recurrent Graph Transformer to handle video inputs, STSGs, and text prompts, thus ensuring an extensive and detailed analysis of video data.
2. Video-of-Thought Framework:
   • Task Decomposition: Drawing inspiration from human cognitive processes, the VoT framework breaks down video reasoning tasks into sequential steps. This involves:
     • Target identification within the video.
     • Object tracking through STSG analysis.
     • Comprehensive action analysis integrating both observed data and commonsense knowledge.
     • Multi-choice question answering via the ranking of answers for the original query.
     • Verification of answers both through perceptive grounding and cognitive commonsense reasoning to ensure factual accuracy.
3. Empirical Evaluation:
   • Benchmarks: The framework's effectiveness is demonstrated through its application across complex video QA benchmarks like VLEP, STAR, IntentQA, Social-IQ, Causal-VidQA, and NExT-QA. In these experiments, VoT outperformed state-of-the-art methods, showing clear superiority in both fine-tuning and zero-shot settings.
   • Enhancement in Zero-shot Performance: MotionEpic, when combined with VoT, displayed remarkable improvements in zero-shot settings across datasets, further underscoring the robustness of the approach.

Implications and Future Directions

The development of MotionEpic and the VoT framework has substantial implications for both the academic paper of AI and practical applications in fields requiring robust video understanding. By embedding structured representations such as STSGs into video AI models, this approach not only fosters deeper insights into video content but also bridges the gap between perception and cognitive reasoning, thus aligning machine understanding more closely with human reasoning processes.

Theoretically, the success of the VoT framework indicates that structured task decomposition, when paired with sophisticated grounding techniques, can significantly boost reasoning capabilities in AI. This could lead to the creation of more versatile AI systems capable of performing complex multimodal tasks involving intricate interactions and dependencies.

Practically, integrating such robust video reasoning frameworks could enhance applications in surveillance, autonomous vehicles, human-computer interaction, and multimedia content analysis. Furthermore, as the framework can operate in zero-shot settings, it offers promising avenues for developing AI solutions with minimal need for extensive in-domain training data.

In conclusion, "Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition" provides a robust advancement in the field of video understanding. By leveraging structured grounding through STSGs and strategic task decomposition via the VoT framework, it opens up new possibilities for AI systems that require high-level reasoning abilities to interpret and interact with real-world video data.