Multi-Granularity Video Memory for Long-Stream Information Processing
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Solution Overview
Problem
Conventional video understanding methods face bottlenecks in storage and calculation efficiency when processing long video streams due to a lack of effective information compression and memory mechanisms, hindering real-time analysis and understanding.
Innovation Solution
A method that updates memory information based on video frames with multiple types of memory features of varying granularities, including spatial, temporal, abstract, and retrieval memories, and generates a memory feature representation to support real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire video is loaded into the model for analysis, then the video understanding can be comprehensive, but the storage and calculation efficiency deteriorates when processing long video streams
Solution Approach 1:
The patent segments the video processing into two distinct parts: a memory component that stores compressed visual information at different granularities (spatial, temporal, abstract) and a model component that processes queries. This segmentation allows comprehensive video understanding to be maintained through the memory while improving processing efficiency by only loading relevant memory features into the model for analysis, rather than loading the entire video.
Solution Approach 2:
The patent extracts key visual information from video frames and stores it in the memory component with multiple levels of feature granularity. This extraction process compresses the visual information while preserving essential characteristics, enabling comprehensive understanding to be maintained with reduced computational resources during query processing.
2Duration of action of stationary object
If conventional solutions process continuous video frames, then the video analysis can be continuous, but the information compression and memory mechanisms are insufficient, resulting in inability to efficiently store and retrieve key information with long time sequences
Solution Approach 1:
The patent segments memory features into four distinct types with different granularities: spatial memory for instant visual processing, temporal memory for long-term integration, abstract memory for advanced semantics, and retrieval memory for precise details. This segmentation enables efficient storage and retrieval of key information from long video sequences while maintaining manageable system complexity through modular memory organization.
Solution Approach 2:
The patent introduces a multi-dimensional memory structure that organizes visual information across different granularity dimensions (spatial, temporal, abstract). This dimensional organization enables efficient compression and retrieval mechanisms by transforming the problem from a single-dimensional sequence processing to a multi-dimensional feature space, improving both continuous processing capability and information retrieval efficiency.
3Productivity
If multiple types of memory features with different granularities are implemented, then the information compression efficiency improves, but the system complexity increases
Solution Approach 1:
The patent implements a universal memory structure where each memory type (spatial, temporal, abstract, retrieval) serves multiple functions: compression, storage, and retrieval operations. This multi-functional design achieves high information compression efficiency through standardized operations across different memory types while managing system complexity by using a unified architectural pattern rather than separate specialized systems.
Data Source
AI summary
Methods, apparatus, devices and computer-readable storage media for information processing are provided. In a method, at least one video frame of a target video is obtained, memory information associated with the target video is updated based on the at least one video frame, and the memory information includes a plurality of types of memory features associated with different levels of feature granularity. In response to receiving a target request for the target video, a memory feature representation is generated based on the memory information, and the target request and the memory feature representation are provided to a target model to obtain a reply generated by the target model.