Space-time memory network for video object segmentation
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Solution Overview
Problem
Existing video editing tools require extensive user interactions and are computationally expensive, leading to inefficiencies in selecting and segmenting target objects across multiple video frames due to inaccuracies and error accumulation, especially when objects change appearance or position.
Innovation Solution
A space-time memory network is employed to classify target objects by generating query and memory key maps, computing memory weights, and performing a weighted summation to accurately segment objects across frames, reducing the need for online training and improving adaptability to appearance changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video editing tools are used to segment target objects across multiple frames, then user interaction time increases, but segmentation accuracy deteriorates due to error accumulation and inability to adapt to appearance changes
Solution Approach 1:
The patent introduces an external memory module as an intermediary between the neural network and segmentation task. This memory stores classification data from multiple frames and provides it to the network during inference, enabling the system to adapt to appearance changes without requiring extensive user interaction or online training
Solution Approach 2:
The system performs preliminary classification of target objects in multiple frames and stores this data in external memory before the actual segmentation task. This pre-computed classification data serves as a foundation that improves subsequent segmentation accuracy while reducing the need for real-time user input
2Adaptability or versatility
If online learning methods are used to adapt to appearance changes, then adaptability improves, but computational resources increase due to required training iterations
Solution Approach 1:
Instead of performing computationally expensive online training iterations, the system creates copies of classification data from multiple frames and stores them in external memory. The neural network can then query this pre-stored data during inference, achieving adaptability to appearance changes without the computational overhead of retraining
Solution Approach 2:
The patent replaces the mechanical process of online training (which requires iterative computation and adjustment of network parameters) with a memory-based retrieval system. The external memory stores pre-computed classification results that can be quickly accessed and applied, substituting the heavy computational mechanics of online learning with a lighter information retrieval mechanism
3Productivity
If mask propagation is used to track objects across frames, then processing speed improves, but segmentation precision deteriorates due to mask smearing and drift
Solution Approach 1:
The system implements feedback by storing classification data from multiple frames in external memory and using this accumulated information to correct and refine segmentation masks. The memory provides continuous feedback about the target object's appearance across different frames, preventing mask drift and smearing while maintaining processing speed
Data Source
AI summary
Certain aspects involve using a space-time memory network to locate one or more target objects in video content for segmentation or other object classification. In one example, a video editor generates a query key map and a query value map by applying a space-time memory network to features of a query frame from video content. The video editor retrieves a memory key map and a memory value map that are computed, with the space-time memory network, from a set of memory frames from the video content. The video editor computes memory weights by applying a similarity function to the memory key map and the query key map. The video editor classifies content in the query frame as depicting the target feature using a weighted summation that includes the memory weights applied to memory locations in the memory value map.