RepAttn3D: Re-parameterizing 3D attention with spatiotemporal augmentation for video understanding
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Summary
This summary is machine-generated.This study introduces a new SpatioTemporally Augmented 3D Attention (STA-3DA) module to improve video understanding. The method enhances feature learning and reduces computational costs in Transformer models for video analysis.
Area Of Science
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background
- Structural re-parameterization is common in image tasks using CNNs and MLPs.
- Integrating re-parameterization with attention mechanisms in video analysis is underexplored.
- Video analysis faces high computational costs, especially during inference.
Purpose Of The Study
- To investigate re-parameterization of 3D attention mechanisms for video understanding.
- To incorporate a spatiotemporal coherence prior to enhance video feature learning.
- To address computational challenges in video analysis tasks.
Main Methods
- Proposing a SpatioTemporally Augmented 3D Attention (STA-3DA) module for Transformer architectures.
- Integrating 3D, spatial, and temporal attention branches during training.
- Merging attention branches into a single 3D operation with learned weights during testing.
Main Results
- The STA-3DA module learns more robust video features with negligible inference overhead.
- The proposed module effectively replaces standard 3D attention in Transformer models, improving performance.
- Achieved competitive video understanding performance on Kinetics-400 and Something-Something V2 datasets.
Conclusions
- The STA-3DA module offers an efficient and effective approach to enhance video understanding.
- Re-parameterization of 3D attention with spatiotemporal priors is a promising direction for video analysis.
- The method provides a practical solution for reducing computational costs in video Transformer models.
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