Papers › Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, Kiyoharu Aizawa
Can we detect common objects in a variety of image domains without instance-level annotations? In this paper, we present a framework for a novel task, cross-domain weakly supervised object detection, which addresses this question. For this paper, we have access to images with instance-level annotations in a source domain (e.g., natural image) and images with image-level annotations in a target domain (e.g., watercolor). In addition, the classes to be detected in the target domain are all or a subset of those in the source domain. Starting from a fully supervised object detector, which is pre-trained on the source domain, we propose a two-step progressive domain adaptation technique by fine-tuning the detector on two types of artificially and automatically generated samples. We test our methods on our newly collected datasets containing three image domains, and achieve an improvement of approximately 5 to 20 percentage points in terms of mean average precision (mAP) compared to the best-performing baselines.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Weakly Supervised Object Detection | Clipart1k | DT+PL | MAP | 46.0 | #5 of 7 | Archive leaderboard | report |
| Weakly Supervised Object Detection | Comic2k | DT+PL (+extra) | MAP | 42.2 | #5 of 8 | Archive leaderboard | report |
| Weakly Supervised Object Detection | Comic2k | DT+PL | MAP | 37.2 | #6 of 8 | Archive leaderboard | report |
| Weakly Supervised Object Detection | Watercolor2k | DT+PL (+extra) | MAP | 59.1 | #5 of 12 | Archive leaderboard | report |
| Weakly Supervised Object Detection | Watercolor2k | DT+PL | MAP | 54.3 | #9 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.
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