Papers › Adaptive Object Detection with Dual Multi-Label Prediction
Adaptive Object Detection with Dual Multi-Label Prediction
Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye
In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliary task. The model exploits multi-label prediction to reveal the object category information in each image and then uses the prediction results to perform conditional adversarial global feature alignment, such that the multi-modal structure of image features can be tackled to bridge the domain divergence at the global feature level while preserving the discriminability of the features. Moreover, we introduce a prediction consistency regularization mechanism to assist object detection, which uses the multi-label prediction results as an auxiliary regularization information to ensure consistent object category discoveries between the object recognition task and the object detection task. Experiments are conducted on a few benchmark datasets and the results show the proposed model outperforms the state-of-the-art comparison methods.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image-to-Image Translation | Cityscapes-to-Foggy Cityscapes | MCAR | mAP | 38.8 | #3 of 6 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | Cityscapes to Foggy Cityscapes | MCAR | mAP@0.5 | 38.8 | #18 of 22 | Archive leaderboard | report |
| Weakly Supervised Object Detection | Watercolor2k | MCAR | MAP | 56.0 | #7 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections