Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection (1909.00597v1)

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.

• The paper introduces Weak Self-Training (WST) and Adversarial Background Score Regularization (BSR) to enhance unsupervised domain adaptation in one-stage object detection.
• Weak Self-Training improves stability by filtering pseudo-labels and carefully selecting negative examples based on a reliable score, mitigating the effects of label inaccuracies.
• Adversarial Background Score Regularization uses adversarial learning to improve foreground-background separation, and combining it with WST yields significant object detection performance gains on benchmark datasets.

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

The paper "Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection", authored by Kim et al., presents a method focused on enhancing the performance of one-stage object detectors in unsupervised domain adaptation scenarios. In essence, the paper introduces two novel strategies: Weak Self-Training (WST) and Adversarial Background Score Regularization (BSR), both aimed at tackling the challenges associated with domain shift in object detection tasks.

The authors build upon the premise that supervised object detection models typically underperform when the training data distribution (source domain) diverges from that of the test data (target domain). To counter this, the paper leverages domain adaptation techniques to transfer knowledge, specifically employing self-training as a mechanism to facilitate class-wise domain adaptation. However, naive self-training approaches that directly utilize pseudo-labels as ground truths are prone to performance degradation due to inaccuracies in those pseudo-labels.

Outline of Methodology

1. Weak Self-Training (WST): The core of WST lies in minimizing the adverse effects of false positives and false negatives in pseudo-labels. The paper describes a mechanism for stabilizing the learning process by masking the gradients of examples considered unreliable. This involves selecting reliable pseudo-labels based on a newly defined Supporting Region-based Reliable Score (SRRS). By adjusting the selection of negative examples, the method discards examples likely to be false negatives to prevent detrimental bias during the training.
2. Adversarial Background Score Regularization (BSR): The BSR component addresses the common problem in both source and target domains: the variation and challenge in background features. The method employs adversarial learning to enhance the separation of foregrounds from backgrounds, leveraging a modified binary cross-entropy loss to enable discriminative feature extraction. This adversarial regularization is applied selectively to the output of the object detector, reducing the risk of aligning domain-specific background artifacts.

Experimental Findings and Implications

The experimental evaluation was carried out on benchmark datasets including Clipart1k, Watercolor2k, and Comic2k, using Pascal VOC datasets as the source. The proposed methods were benchmarked against a baseline SSD model and other self-training approaches. Results indicate that both WST and BSR independently improve detection accuracy, with a notable performance increase when the two strategies are combined. For instance, on Clipart1k, the combined approach achieved an mAP of 35.7%, significantly outperforming other domain adaptation baselines.

These findings suggest that WST and BSR, while individually addressing distinct facets of the domain adaptation challenge, offer complementary benefits when integrated. Practically, this dual strategy holds potential for improving real-world application of object detection models where labeling in the target domain is scarce or infeasible.

Theoretical Contribution and Future Directions

The paper contributes to the theoretical framework of domain adaptation in object detection, extending the frontier of how adversarial training and pseudo-label refinement can be unified within a one-stage detection context. Future research could explore the application of these methods to other domains with high variability in background conditions, as well as the integration of more sophisticated pseudo-label generation techniques to further mitigate false-positive impacts.

In conclusion, the paper by Kim et al. exemplifies a pragmatic approach to advancing object detection performance across varied domains without the need for extensive manual labeling. The insights and methodologies introduced not only address current limitations but also lay a foundation for further innovations in unsupervised domain adaptive detection systems.