Self-supervised Video Transformer for Human Action Recognition
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Solution Overview
Problem
Existing self-supervised learning methods for video transformers face challenges in effectively handling varying spatial and temporal resolutions, requiring large batch sizes, negative sample mining, and multiple networks for different resolutions, which limits their adaptability and efficiency in human action recognition tasks.
Innovation Solution
The proposed Self-supervised Video Transformer (SVT) jointly varies spatial and temporal resolutions using a predictive objective for self-supervision, employing a student-teacher framework to learn cross-view and motion correspondences across dimensions, eliminating the need for negative mining and large batch sizes, and allowing a single shared network to process varying resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing self-supervised learning methods use multiple networks for different resolutions, then they can handle varying spatial and temporal resolutions, but the device complexity increases
Solution Approach 1:
The patent employs a single video transformer network that can process inputs of varying spatial and temporal resolutions without requiring separate networks for different resolutions. This universal network architecture handles multiple resolution scenarios through a unified processing pipeline, eliminating the need for multiple specialized networks while maintaining adaptability to different input formats
Solution Approach 2:
The patent implements dynamic resolution handling where the video transformer processes clips at their native varying resolutions rather than forcing uniform resolution. The system dynamically adapts to different spatial and temporal resolutions through flexible input processing and attention mechanisms that work effectively across varying scales without requiring pre-processing to fixed dimensions
2Reliability
If existing methods use large batch sizes for training, then they can achieve better performance, but the productivity decreases due to increased computational requirements
Solution Approach 1:
The patent implements self-distillation where the video transformer generates its own supervision signals by predicting future frames and comparing predictions with actual future frames. This self-supervised approach eliminates the need for large labeled datasets and reduces dependency on large batch sizes for effective training, allowing efficient training with smaller batches while maintaining performance
Solution Approach 2:
The system uses predictive objectives where the model's own predictions serve as feedback signals for training. The video transformer predicts future video frames and uses the prediction error as a self-generated supervision signal, creating an internal feedback loop that enables effective learning without requiring large external datasets or large batch sizes for statistical reliability
3Reliability
If existing methods require negative sample mining, then they can improve representation learning, but the loss of time increases due to the mining process
Solution Approach 1:
The patent uses self-distillation where the model generates its own supervision signals through frame prediction tasks. This self-generated supervision eliminates the need for external negative sample mining processes, as the model learns from its own prediction errors and temporal relationships within the video data itself, significantly reducing training time while maintaining effective representation learning
Solution Approach 2:
The patent converts the challenge of having no labeled data into a benefit by using self-supervised learning through frame prediction. Instead of requiring time-consuming negative sample mining from external sources, the system benefits from the temporal structure inherent in video data, using the natural temporal relationships between frames as the learning signal, thereby eliminating the need for separate negative mining processes
4Device complexity
If a single shared network is used for varying resolutions, then the device complexity decreases, but the adaptability to handle different spatial and temporal resolutions worsens
Solution Approach 1:
The video transformer implements dynamic resolution handling through attention mechanisms that adaptively process inputs at varying spatial and temporal scales. The self-attention operations dynamically adjust to different input dimensions, allowing a single network architecture to effectively handle diverse resolutions without requiring separate specialized networks for each resolution level
Solution Approach 2:
The system handles varying resolutions by dynamically adjusting processing parameters such as patch sizes, attention window dimensions, and temporal sampling rates based on input characteristics. This parameter adaptation allows a single shared network to maintain high performance across different spatial and temporal resolutions without requiring architectural modifications or multiple specialized networks
Data Source
AI summary
A system, computer readable medium and method trains a video transformer, using a machine learning engine, for human action recognition in a video. The method includes sampling video clips with varying temporal resolutions in global views and sampling the video clips from different spatiotemporal windows in local views. The machine learning engine is configured to match the global and local views in a framework of student-teacher networks to learn cross-view correspondence between local and global views, and to learn motion correspondence between varying temporal resolutions. The video transformer can output for display video clips in a manner that emphasizes attention to the recognized human action.