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Reusable Architecture Growth for Continual Stereo Matching
Summary
This study introduces a Reusable Architecture Growth (RAG) framework for continual stereo matching. It enables models to learn new scenes without forgetting old ones, improving disparity prediction for real-world applications.
Area of Science:
- Computer Vision
- Machine Learning
- Deep Learning
Background:
- Recent stereo depth estimation models utilize convolutional neural networks for dense disparity regression.
- Continuous acquisition of training data in practical applications necessitates continual learning capabilities for models.
- Existing models often struggle with forgetting previously learned scenes when adapting to new data.
Purpose of the Study:
- To develop a continual stereo matching framework capable of learning new scenes, preventing catastrophic forgetting, and performing continuous disparity prediction.
- To introduce a novel Reusable Architecture Growth (RAG) framework for adaptive and efficient continual learning in stereo matching.
- To enhance the adaptability of stereo depth estimation models for practical, real-world deployment.
Main Methods:
- Proposed a Reusable Architecture Growth (RAG) framework employing task-specific neural unit search and architecture growth.
- Implemented both supervised and self-supervised learning approaches within the RAG framework for continual scene learning.
- Introduced a Scene Router module for adaptive selection of scene-specific architectural paths during inference.
Main Results:
- The RAG framework demonstrated high reusability of previous neural units while achieving strong performance on new scenes.
- The proposed method significantly outperformed state-of-the-art methods, especially in challenging cross-dataset scenarios.
- Experiments confirmed the framework's impressive performance across diverse environmental conditions (weather, road, city).
Conclusions:
- The RAG framework effectively addresses the challenge of continual stereo matching, enabling models to adapt to new scenes without forgetting.
- The Scene Router module enhances inference adaptability, facilitating end-to-end stereo architecture learning.
- This approach holds significant potential for practical deployment in real-world stereo depth estimation applications.
