Papers › Multiple instance active learning for object detection
Multiple instance active learning for object detection
Tianning Yuan, Fang Wan, Mengying Fu, Jianzhuang Liu, Songcen Xu, Xiangyang Ji, Qixiang Ye
Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection. In this paper, we propose Multiple Instance Active Object Detection (MI-AOD), to select the most informative images for detector training by observing instance-level uncertainty. MI-AOD defines an instance uncertainty learning module, which leverages the discrepancy of two adversarial instance classifiers trained on the labeled set to predict instance uncertainty of the unlabeled set. MI-AOD treats unlabeled images as instance bags and feature anchors in images as instances, and estimates the image uncertainty by re-weighting instances in a multiple instance learning (MIL) fashion. Iterative instance uncertainty learning and re-weighting facilitate suppressing noisy instances, toward bridging the gap between instance uncertainty and image-level uncertainty. Experiments validate that MI-AOD sets a solid baseline for instance-level active learning. On commonly used object detection datasets, MI-AOD outperforms state-of-the-art methods with significant margins, particularly when the labeled sets are small. Code is available at https://github.com/yuantn/MI-AOD.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Active Object Detection | COCO (Common Objects in Context) | RetinaNet | AP | (7.3, 13.8, 16.9, 19.1, 20.8) on 2% ~ 10% | #1 of 1 | Archive leaderboard | report |
| Active Object Detection | PASCAL VOC 07+12 | RetinaNet | mAP | (47.18, 58.41, 64.02, 67.72, 69.79, 71.07, 72.27) on 5% ~ 20% | #1 of 2 | Archive leaderboard | report |
| Active Object Detection | PASCAL VOC 07+12 | SSD | mAP | (53.62, 62.86, 66.83, 69.33, 70.80, 72.21, 72.84, 73.74, 74.18, 74.91) on 1k ~ 10k | #2 of 2 | 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.
Methods
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