Multiple Adverse Weather Conditions Adaptation for Object Detection via Causal Intervention.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• State-of-the-art object detection models struggle with visual variations and out-of-distribution data due to domain gaps, particularly adverse weather.
• Existing domain adaptation methods often require extensive domain-specific training samples.

Purpose of the Study:

• To develop a novel domain adaptation model for discovering weather condition invariant feature representations in object detection.
• To bridge the domain gap caused by adverse weather conditions without requiring large amounts of domain-specific data.

Main Methods:

• A memory network is employed to create a confounder dictionary storing object feature prototypes under various scenarios.
• A dynamic item extraction strategy is used to ensure prototype representativeness within the memory dictionary.
• A causal intervention reasoning module explores invariant object representations across different weather conditions, enhanced by categorical consistency regularization.

Main Results:

• The proposed model achieves state-of-the-art performance on multiple benchmarks, including RTTS, Foggy-Cityscapes, RID, and BDD 100K.
• Demonstrates effectiveness in object detection under diverse and adverse weather conditions.
• Successfully discovers weather-invariant feature representations.

Conclusions:

• The novel domain adaptation model effectively addresses the challenge of domain gaps in object detection caused by weather variations.
• The approach of using a memory network and causal intervention reasoning provides a robust method for learning invariant representations.
• The findings suggest a promising direction for improving the reliability and generalizability of object detection systems in real-world, dynamic environments.