Video Description Model With Token Compression and Temporal Pooling
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Solution Overview
Problem
Existing large vision language models perform poorly on video understanding tasks compared to image understanding tasks, necessitating improved techniques for enhancing their video description generation capabilities.
Innovation Solution
A three-branch architecture machine learning model that processes video frames through temporal pooling, frame-level pooling, and cross-attention, combined with a large language model, to generate accurate video descriptions by aligning and distinguishing different types of tokens using indicator tokens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing large vision language models are used for video understanding tasks, then the model structure is simple and easy to implement, but the model performance is poor compared to image understanding tasks
Solution Approach 1:
The model architecture is segmented into three distinct branches: a temporal pooling branch that aggregates information across time, a frame-level pooling branch that processes individual frames, and a cross-attention branch that selectively attends to relevant frames. This segmentation allows each branch to specialize in different aspects of video understanding, improving overall performance while maintaining modular complexity
Solution Approach 2:
The patent introduces temporal dimension processing by adding temporal pooling operations that aggregate visual tokens across multiple frames. This adds a time dimension to the existing spatial processing, transforming the model from handling static images to understanding dynamic video sequences without completely redesigning the base architecture
2Loss of information
If all visual tokens from all video frames are fed into the language model, then complete video information is preserved, but the number of tokens becomes excessively large
Solution Approach 1:
The frame-level pooling branch extracts key visual information from individual frames by selecting representative visual tokens. This extraction process identifies and retains only the most informative tokens from each frame, reducing the total token count while preserving essential video content
Solution Approach 2:
The temporal pooling branch performs preliminary aggregation of visual tokens across all frames before feeding them to the language model. By pre-processing and compressing temporal information in advance, the model reduces the token burden on subsequent processing stages while maintaining comprehensive video understanding
3Productivity
If the model processes each frame independently, then processing efficiency is improved, but temporal relationships between frames are lost
Solution Approach 1:
The patent merges the outputs of three separate branches (temporal pooling, frame-level pooling, and cross-attention) into a unified representation that feeds into the language model. This combination integrates temporal relationships from the temporal pooling branch with frame-level details and selective attention information, achieving both efficiency and temporal understanding
Data Source
AI summary
The present disclosure describes techniques for generating video descriptions using a machine learning model. A plurality of sets of visual tokens corresponding to a plurality of frames of a video is generated. A first type of tokens is generated by implementing temporal pooling on the plurality of sets of visual tokens corresponding to the plurality of frames. A second type of tokens is generated by compressing each of the plurality of sets of visual tokens corresponding to each of the plurality of frames. A third type of tokens is generated by applying cross-attention between each of the plurality of sets of visual tokens and a fourth type of tokens including text tokens generated based on an input text query. A text description of the video is generated based on the first type of tokens, the second type of tokens, the third type of tokens, and the fourth type of tokens.