Action Localization Using Learned Queries and Attention
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Solution Overview
Problem
Current video processing techniques for action localization require pre-processing and post-processing steps like region proposals and non-maximal suppression, which are computationally expensive and inefficient, especially when training data is suboptimal with sparse bounding box annotations.
Innovation Solution
The system employs learned queries in combination with a video encoder neural network and a decoder neural network, using factorized self-attention and cross-attention mechanisms to perform action localization without additional pre-processing or post-processing, allowing for efficient and accurate action localization even with sparse annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional action localization techniques are used with region proposals and non-maximal suppression, then action localization can be performed, but computational cost and processing time increase significantly
Solution Approach 1:
The patent extracts and removes the computationally expensive pre-processing (region proposals) and post-processing (non-maximal suppression) steps from the traditional action localization pipeline. By using learned queries that directly encode spatial and temporal information, the system achieves action localization without these additional processing stages, thereby improving computational efficiency while maintaining accuracy.
Solution Approach 2:
The patent changes the parameter representation from traditional region proposal formats to learned query vectors that directly encode bounding box parameters (spatial coordinates and dimensions). This parameter transformation allows the model to directly output final action localization results without requiring subsequent non-maximal suppression operations, resolving the contradiction between accuracy and computational efficiency.
2Adaptability or versatility
If sparse bounding box annotations are used for training, then training data requirements are reduced, but action localization performance deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the video encoder and decoder components separately on abundant unsupervised video data before joint fine-tuning. This pre-training establishes strong spatio-temporal feature representations that enable the model to achieve good action localization performance even when trained with sparse bounding box annotations, thus resolving the contradiction between training data flexibility and accuracy.
Solution Approach 2:
The patent introduces learned queries as an intermediary between the video encoder features and the action localization output. These queries act as a bridge that learns to associate spatio-temporal regions with actions, enabling effective training with sparse annotations by providing a structured intermediate representation that captures both spatial and temporal dependencies.
3Measurement precision
If full tubelet annotations are used for training, then action localization accuracy improves, but data annotation effort and storage requirements increase
Solution Approach 1:
The patent applies partial action by demonstrating that complete tubelet annotations for every frame are not necessary. Instead, sparse annotations at key frames combined with the model's learned spatio-temporal associations through factorized attention mechanisms provide sufficient training signal, reducing annotation burden while maintaining competitive performance.
4Productivity
If factorized self-attention and cross-attention mechanisms are used, then computational efficiency improves, but model complexity increases
Solution Approach 1:
The patent segments the attention computation into factorized self-attention and cross-attention components with distinct functional roles. Self-attention handles spatio-temporal associations within the video sequence, while cross-attention handles query-feature associations. This segmentation allows for optimized computational patterns that improve efficiency despite the increased architectural complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing action localization on an input video. In particular, a system maintains a set of query vectors and uses the input video and the set of query vectors to generate an action localization output for the input video. The action localization output includes, for each of one or more agents depicted in the video, data specifying, for each of one or more video frames in the video, a respective bounding box in the video frame that depicts the agent and a respective action from a set of actions that is being performed by the agent in the video frame.