De-Simplifying Pseudo Labels to Enhancing Domain Adaptive Object Detection

TL;DR

This paper introduces DeSimPL, a novel method that reduces simple-label bias in self-labeling for domain adaptive object detection, significantly improving performance by mitigating false positives and enhancing training sample complexity.

Contribution

It proposes DeSimPL, which uses an instance-level memory bank, adversarial samples, and adaptive weighted loss to improve self-labeling in domain adaptive object detection.

Findings

DeSimPL reduces simple sample bias during training.

The method significantly improves detection accuracy on four benchmarks.

DeSimPL outperforms existing domain alignment and self-labeling methods.

Abstract

Despite its significant success, object detection in traffic and transportation scenarios requires time-consuming and laborious efforts in acquiring high-quality labeled data. Therefore, Unsupervised Domain Adaptation (UDA) for object detection has recently gained increasing research attention. UDA for object detection has been dominated by domain alignment methods, which achieve top performance. Recently, self-labeling methods have gained popularity due to their simplicity and efficiency. In this paper, we investigate the limitations that prevent self-labeling detectors from achieving commensurate performance with domain alignment methods. Specifically, we identify the high proportion of simple samples during training, i.e., the simple-label bias, as the central cause. We propose a novel approach called De-Simplifying Pseudo Labels (DeSimPL) to mitigate the issue. DeSimPL utilizes an instance-level memory bank to implement an innovative pseudo label updating strategy. Then, adversarial samples are introduced during training to enhance the proportion. Furthermore, we propose an adaptive weighted loss to avoid the model suffering from an abundance of false positive pseudo labels in the late training period. Experimental results demonstrate that DeSimPL effectively reduces the proportion of simple samples during training, leading to a significant performance improvement for self-labeling detectors. Extensive experiments conducted on four benchmarks validate our analysis and conclusions.