Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection

TL;DR

This paper proposes a novel unsupervised domain adaptation method for one-stage object detection, combining weak self-training and adversarial background regularization to improve detection accuracy across different data distributions.

Contribution

It introduces a weak self-training approach and adversarial background score regularization, which together enhance class-wise and background discrimination in domain adaptive detection.

Findings

Significant performance improvement in unsupervised domain adaptation for object detection.

WST reduces negative impact of pseudo-label errors, stabilizing training.

BSR improves feature discrimination between foregrounds and backgrounds.

Abstract

Deep learning-based object detectors have shown remarkable improvements. However, supervised learning-based methods perform poorly when the train data and the test data have different distributions. To address the issue, domain adaptation transfers knowledge from the label-sufficient domain (source domain) to the label-scarce domain (target domain). Self-training is one of the powerful ways to achieve domain adaptation since it helps class-wise domain adaptation. Unfortunately, a naive approach that utilizes pseudo-labels as ground-truth degenerates the performance due to incorrect pseudo-labels. In this paper, we introduce a weak self-training (WST) method and adversarial background score regularization (BSR) for domain adaptive one-stage object detection. WST diminishes the adverse effects of inaccurate pseudo-labels to stabilize the learning procedure. BSR helps the network extract discriminative features for target backgrounds to reduce the domain shift. Two components are complementary to each other as BSR enhances discrimination between foregrounds and backgrounds, whereas WST strengthen class-wise discrimination. Experimental results show that our approach effectively improves the performance of the one-stage object detection in unsupervised domain adaptation setting.