Transformer Action Localization With Weak Labels and Pseudo-Labels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Annotating the starting and end frames of actions in videos is labor-intensive and difficult due to the reliance on human annotators, making it challenging to train models effectively for temporal action localization.
Innovation Solution
A neural network model processes appearance and motion modalities from video streams using transformer branches with self-attention and cross-attention, leveraging weakly annotated data through a mutual learning loss and pseudo-labels to identify action start and end times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation by human annotators is used to identify actions and timing information, then measurement precision of action boundaries is improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The system uses pseudo-label generation where the model annotates itself by predicting action boundaries from weakly annotated video data. The neural network processes video frames and generates its own training annotations, eliminating the need for time-consuming manual human annotation while maintaining acceptable precision through iterative refinement of pseudo-labels
Solution Approach 2:
The system performs preliminary action by using weakly annotated data (video-level labels without temporal boundaries) as input, and automatically generates precise temporal annotations through pseudo-labeling before final model training. This preliminary automated annotation step replaces the need for manual precise annotation
2Manufacturing precision
If manual annotation by human annotators is used to identify actions and timing information, then manufacturing precision of annotated training data is improved, but device complexity and ease of manufacture worsen
Solution Approach 1:
The system generates its own high-precision training annotations automatically through pseudo-labeling. The neural network processes weakly annotated video data and produces precise temporal action boundaries without requiring external human annotators, making the data manufacturing process self-sufficient and easily scalable
Solution Approach 2:
The system introduces pseudo-labels as an intermediary between weak video-level annotations and precise temporal action boundaries. These pseudo-labels serve as intermediate training signals that bridge the gap between coarse annotations and fine-grained action localization, enabling precise training without manual annotation
3Ease of manufacture
If weakly annotated training data is used without precise timing information, then ease of obtaining training data is improved, but measurement precision of action localization deteriorates
Solution Approach 1:
The system performs preliminary processing of weakly annotated video data by generating pseudo-labels that contain precise temporal action boundaries. This preliminary pseudo-label generation step transforms coarse video-level annotations into fine-grained temporal annotations, enabling precise action localization while maintaining ease of data acquisition
Solution Approach 2:
The system uses feedback through iterative pseudo-label refinement where the model's predictions are fed back into the training process. The generated pseudo-labels are used to retrain and refine the model, progressively improving measurement precision of action timing while continuing to use only weakly annotated data
Data Source
AI summary
Methods and systems for temporal action localization include processing a video stream to identify an action and a start time and a stop time for the action using a neural network model that separately processes information of appearance and motion modalities from the video stream using transformer branches that include a self-attention and a cross-attention between the appearance and motion modalities. An action is performed responsive to the identified action.