Multi-Range Feature Memory for Long-Context Video Segmentation
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Solution Overview
Problem
Current neural networks face challenges in handling long-term context in videos due to high memory requirements and computational costs, particularly in real-time online video processing applications, leading to inaccurate action analysis in video segmentation tasks.
Innovation Solution
A system and method for sequential modeling using a memory with multi-range arrays that store feature maps, allowing for efficient long-range context processing in video segmentation, reducing memory and computation costs while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recurrent networks maintain historical memory by adding each frame features with a decay factor, then long-range context can be captured, but far range features are significantly decayed and memory requirements increase
Solution Approach 1:
The memory is divided into multiple banks, each bank storing features from a specific time range. This segmentation allows the system to capture long-range context without requiring a single large memory structure, reducing overall memory requirements while maintaining accuracy for both near and far range features.
Solution Approach 2:
The system dynamically adjusts which memory banks are accessed based on the current processing needs. Different banks store features from different temporal ranges, allowing the network to adaptively retrieve appropriate context lengths without maintaining all possible historical features in memory simultaneously.
2Reliability
If a large window size is used in sliding window method to capture long-range context, then long-range dependencies can be identified, but processing time and computation cost increase
Solution Approach 1:
The sliding window approach is segmented into multiple fixed-size windows, each associated with a specific memory bank. This allows parallel processing of different time ranges without requiring a single large window, reducing computation cost while maintaining the ability to capture long-range dependencies through coordinated access to multiple banks.
Solution Approach 2:
Features are pre-computed and stored in memory banks before the segmentation network needs them. This preliminary action allows the network to quickly retrieve pre-processed features from memory during inference, reducing real-time computation cost while maintaining accuracy for long-range dependency detection.
3Productivity
If a small window size is used in sliding window method, then processing time is reduced, but accuracy deteriorates due to short range context
Solution Approach 1:
The system uses multiple memory banks that can be selectively accessed to provide different context lengths. This multi-functionality allows the same segmentation network to achieve both fast processing (by accessing only recent banks) and high accuracy (by accessing additional historical banks when needed), without requiring separate models for different window sizes.
Data Source
AI summary
A system for video segmentation may include a neural network and a memory including multi-range arrays. The multi-range arrays may store feature map arrays including different number of feature maps. The system may generate a feature map from a frame in a video at a time and store the feature map in the memory. The feature map may be in a feature map array that also includes one or more contextual feature maps generated from other frames in the video. The system uses the feature map array to determine whether the frame falls into a segment of the video. The system may generate a new feature map later from another frame and include the new feature map in a new feature map array that also includes the first feature map. The system uses the new feature map array to determine whether the new frame falls into a segment.