Sparse Temporal Pooling Network for Weakly Supervised Action Localization
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Solution Overview
Problem
Current methods for action recognition in videos face challenges due to the complex nature of video data, large file sizes, and the difficulty in modeling temporal information, leading to suboptimal performance in untrimmed videos without temporal annotations, which are costly and time-consuming to annotate.
Innovation Solution
A computer-implemented method using a sparse temporal pooling network with two weakly supervised temporal action localization models analyzes RGB and optical flow streams to generate weighted temporal class activation maps, enabling temporal action localization without explicit temporal annotations by learning from video-level labels and incorporating sparsity and classification losses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep neural networks are applied to video data, then action recognition performance can be improved, but the complexity of modeling temporal information and computational requirements increase significantly
Solution Approach 1:
The video is segmented into multiple clips with different temporal resolutions. The network processes these segmented clips independently and aggregates their features, avoiding the need to model entire long videos at high temporal resolution. This segmentation strategy reduces temporal modeling complexity while maintaining action recognition accuracy.
Solution Approach 2:
The patent transforms the temporal dimension by creating multi-scale temporal representations through different clip sampling strategies. Instead of directly modeling temporal relationships in the original video timeline, the system creates auxiliary temporal dimensions through varied clip lengths and sampling rates, making temporal information more tractable for the neural network.
2Measurement precision
If trimmed videos with temporal annotations are used for training, then action localization accuracy improves, but annotation cost and time consumption increase prohibitively
Solution Approach 1:
The system performs self-supervised learning by using the video content itself to generate training signals. The network learns to localize actions by predicting temporal boundaries from unannotated video clips, using intrinsic video cues rather than external annotations. This self-service approach eliminates the need for expensive temporal annotations while maintaining localization capability.
Solution Approach 2:
The patent pre-trains the network on large amounts of unannotated video data to learn general temporal patterns and action representations. This preliminary learning phase prepares the model to perform accurate action localization with minimal or no temporal annotations during the actual inference stage, effectively performing the localization task in advance of detailed annotation.
3Measurement precision
If multiple temporal scales are used for action detection, then detection accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The video is divided into multiple clips with different temporal resolutions, allowing the network to detect actions at various scales. By processing segmented clips independently rather than analyzing the entire video at all scales simultaneously, the computational complexity is distributed and managed more efficiently.
Solution Approach 2:
The system processes only necessary portions of the video at high temporal resolution, while using lower resolution for other segments. This partial processing approach applies computational resources selectively to regions or time periods where high-precision action detection is most needed, reducing overall computational complexity while maintaining detection accuracy.
Data Source
AI summary
Systems and methods for a weakly supervised action localization model are provided. Example models according to example aspects of the present disclosure can localize and/or classify actions in untrimmed videos using machine-learned models, such as convolutional neural networks. The example models can predict temporal intervals of human actions given video-level class labels with no requirement of temporal localization information of actions. The example models can recognize actions and identify a sparse set of keyframes associated with actions through adaptive temporal pooling of video frames, wherein the loss function of the model is composed of a classification error and a sparsity of frame selection. Following action recognition with sparse keyframe attention, temporal proposals for action can be extracted using temporal class activation mappings, and final time intervals can be estimated corresponding to target actions.