Temporal Action Localization With Mutual Guidance Refinement
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Solution Overview
Problem
Existing computer vision AI models for temporal action localization suffer from inaccurate bounding box predictions and classification errors due to independent performance of localization and classification subtasks, leading to irrelevant segment inclusion and incorrect classifications.
Innovation Solution
Implement a Mutual Task Guidance Module (MTGM) with Loc2Cls and Cls2Loc engines that leverage complementary information from localization and classification subtasks to refine bounding box predictions and classification results, using mutual guidance learning to enhance the performance of temporal action localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If localization and classification subtasks are performed independently, then the system structure is simple, but the localization accuracy and classification precision deteriorate
Solution Approach 1:
The patent merges the localization and classification subtasks into a unified framework where they mutually guide each other. The localization module and classification module are integrated through a shared feature extraction backbone and cross-task guidance mechanisms, allowing them to work together rather than independently, thereby improving both localization accuracy and classification precision simultaneously.
Solution Approach 2:
The patent implements feedback loops where classification results guide localization adjustments and localization outcomes refine classification predictions. The classification module provides feedback to adjust localization bounding boxes, while the localization module feeds back to refine classification predictions, creating a mutual guidance system that continuously improves both tasks' performance.
2Productivity
If localization and classification subtasks are performed independently, then the processing speed is fast, but the classification precision and localization accuracy deteriorate
Solution Approach 1:
The patent performs preliminary feature extraction that serves both localization and classification tasks simultaneously. The shared feature extraction backbone pre-processes video data in a way that prepares information for both subtasks before they execute, reducing redundant processing while maintaining high precision through mutual guidance during the subsequent refinement stages.
3Area of stationary object
If coarse temporal bounding boxes are used, then the processing scope is broad, but irrelevant segments are included
Solution Approach 1:
The classification results provide feedback to the localization module to refine coarse temporal bounding boxes. The classification module identifies relevant segments and feeds this information back to adjust and tighten the temporal bounding boxes, eliminating irrelevant segments while maintaining comprehensive coverage of actual actions.
Solution Approach 2:
The patent applies different processing quality levels to different temporal segments. Rather than uniformly processing all segments at the same level, the system identifies and applies refined processing specifically to segments that contain actual actions, while coarser processing is sufficient for segments determined to be irrelevant, thereby improving precision without unnecessary computational overhead.
Data Source
AI summary
A computer vision temporal action localization (TAL) computing tool and operations are provided. The TAL computing tool receives a coarse temporal bounding box, having a first start point and a first end point, for an action in the input video data, and a first set of logits, where each logit corresponds to a potential classification of the action in the input video data. The TAL computing tool executes a first engine on the coarse temporal bounding box to generate a second set of logits, and a second engine on the first set of logits to generate a refined temporal bounding box having a second start point and a second end point. The TAL computing tool performs the computer vision temporal action localization operation based on the second set of logits and the refined temporal bounding box to specify a temporal segment of the input video data corresponding to an action represented in the input video data, and a corresponding classification of the action represented in the temporal segment.