The Challenge of Long Video Processing with AI

The analysis and understanding of videos using Artificial Intelligence (AI) has made enormous progress in recent years. Large Language Models (LLMs) play a central role in this. Nevertheless, processing long videos remains a challenge. The limited context length of LLMs and the sheer amount of information that can be contained in a long video often lead to information loss and inaccurate results. Existing methods for compressing video information quickly reach their limits and also have difficulties incorporating additional modalities such as audio.

A New Approach: Temporal Dynamic Context (TDC)

A promising approach to solving this problem is the "Temporal Dynamic Context" (TDC) method, which utilizes the temporal relationships between individual frames of a video. This approach is based on segmenting the video into semantically coherent scenes, which are identified based on similarities between the frames. Each frame is then converted into tokens using image and audio encoders.

The core of the TDC approach is a novel compressor for the temporal context. This compressor uses a query-based transformer to aggregate the image, audio, and instruction text tokens into a limited number of context tokens. These tokens represent the most important information of each scene. The static frame tokens and the dynamic context tokens are then passed to the LLM for video analysis.

Chain-of-Thought for Extremely Long Videos

For extremely long videos, a "Chain-of-Thought" (CoT) strategy has been developed. This method allows the LLM to process and understand long videos step by step without the need for additional training. The CoT strategy extracts answers from multiple video segments and uses these intermediate answers as part of the thinking process to arrive at the final answer.

Promising Results and Future Applications

Initial tests of the TDC approach in combination with the CoT strategy show promising results. In experiments with various LLMs and benchmarks, which included general video question answering, long video understanding, and audiovisual video comprehension, the models with TDC achieved good results. These results suggest that the TDC approach is an important step towards a more comprehensive understanding of long videos by AI.

The combination of static visual features and dynamic multimodal contexts allows for more effective integration of visual and auditory information. Research in this area promises further advances in the field of multimodal long video processing and opens up new possibilities for applications in various areas, from automated video analysis to the development of interactive video systems.

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