Action Anticipation Transformer Using Causal Masking for Ego Agents
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in efficiently anticipating human actions due to computational resource constraints, hindering effective data evaluation for action anticipation in applications like autonomous driving and video surveillance.
Innovation Solution
A computer-implemented method and system using an agent action anticipative transformer that receives image data, extracts short-range clips, and applies self-supervision with causal masking to output action predictions and feature predictions, enabling ego-centric action anticipation for autonomous control of ego agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used for action anticipation, then computational resources are consumed, but processing efficiency deteriorates and training time increases
Solution Approach 1:
The video input is divided into multiple short-range clips, and the processing is segmented into distinct stages: clip-level feature extraction, temporal aggregation, and action prediction. This segmentation allows parallel processing of clips and reduces the computational burden on any single processing unit, improving overall efficiency while reducing training time.
Solution Approach 2:
The patent introduces a temporal dimension by processing video clips sequentially and aggregating features across time. The transformer architecture operates in the temporal domain to aggregate clip-level features, enabling efficient long-term action anticipation without requiring excessive computational resources at any single moment.
2Measurement precision
If more data is evaluated for action anticipation, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The evaluation of video data is segmented into multiple short-range clips, allowing the system to process and evaluate data in manageable chunks. This segmentation maintains comprehensive data evaluation for accurate predictions while avoiding the computational complexity of processing entire video sequences at once.
Solution Approach 2:
The patent extracts only the necessary clip-level features from video data rather than processing all possible features. This partial action approach evaluates sufficient data for accurate action anticipation while deliberately limiting the scope to avoid excessive computational complexity.
3Productivity
If traditional architectures are used, then implementation is straightforward, but performance on long-term action anticipation deteriorates
Solution Approach 1:
The patent merges clip-level feature extraction with temporal aggregation in a unified transformer architecture. This combination allows the system to capture both local clip-level patterns and long-term temporal dependencies, achieving superior performance on long-term action anticipation tasks while maintaining a relatively streamlined architecture through the use of self-attention mechanisms.
Data Source
AI summary
A system and method for providing an agent action anticipative transformer that include receiving image data associated with a video of a surrounding environment of an ego agent. The system and method additionally include analyzing the image data and extracting short range clips from the image data. The system and method also include analyzing the short range clips and extracting clip-level features associated with each of the short range clips. The system and method further include executing self-supervision using causal masking with respect to the extracted clip-level features to output action predictions and feature predictions to enable ego-centric action anticipation with respect to at least one target agent to autonomously control the ego agent.