Domain-Specific Suppression for Adaptive Object Detection

• URL: http://arxiv.org/abs/2105.03570v1
• Date: Sat, 8 May 2021 03:11:36 GMT
• Title: Domain-Specific Suppression for Adaptive Object Detection
• Authors: Yu Wang, Rui Zhang, Shuo Zhang, Miao Li, YangYang Xia, XiShan Zhang, ShaoLi Liu
• Abstract summary: We propose a new perspective on how CNN models gain the transferability, viewing the weights of a model as a series of motion patterns. The goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one. In this paper, we propose the domain-specific suppression, an exemplary and generalizable constraint to the original convolution gradients in backpropagation.
• Score: 20.887838391558624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain adaptation methods face performance degradation in object detection, as the complexity of tasks require more about the transferability of the model. We propose a new perspective on how CNN models gain the transferability, viewing the weights of a model as a series of motion patterns. The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one. Current UDA object detection methods view the two directions as a whole while optimizing, which will cause domain-invariant direction mismatch even if the output features are perfectly aligned. In this paper, we propose the domain-specific suppression, an exemplary and generalizable constraint to the original convolution gradients in backpropagation to detach the two parts of directions and suppress the domain-specific one. We further validate our theoretical analysis and methods on several domain adaptive object detection tasks, including weather, camera configuration, and synthetic to real-world adaptation. Our experiment results show significant advance over the state-of-the-art methods in the UDA object detection field, performing a promotion of $10.2\sim12.2\%$ mAP on all these domain adaptation scenarios.

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