Exploiting Low-confidence Pseudo-labels for Source-free Object Detection
Zhihong Chen Zilei Wang Yixin Zhang
Source-free object detection (SFOD) aims to adapt a source-trained detector
to an unlabeled target domain without access to the labeled source data.
Current SFOD methods utilize a threshold-based pseudo-label approach in the
adaptation phase, which is typically limited to high-confidence pseudo-labels
and results in a loss of information. To address this issue, we propose a new
approach to take full advantage of pseudo-labels by introducing high and low
confidence thresholds. Specifically, the pseudo-labels with confidence scores
above the high threshold are used conventionally, while those between the low
and high thresholds are exploited using the Low-confidence Pseudo-labels
Utilization (LPU) module. The LPU module consists of Proposal Soft Training
(PST) and Local Spatial Contrastive Learning (LSCL). PST generates soft labels
of proposals for soft training, which can mitigate the label mismatch problem.
LSCL exploits the local spatial relationship of proposals to improve the
model's ability to differentiate between spatially adjacent proposals, thereby
optimizing representational features further. Combining the two components
overcomes the challenges faced by traditional methods in utilizing
low-confidence pseudo-labels. Extensive experiments on five cross-domain object
detection benchmarks demonstrate that our proposed method outperforms the
previous SFOD methods, achieving state-of-the-art performance.