Box-Level Active Detection

• URL: http://arxiv.org/abs/2303.13089v1
• Date: Thu, 23 Mar 2023 08:06:10 GMT
• Title: Box-Level Active Detection
• Authors: Mengyao Lyu, Jundong Zhou, Hui Chen, Yijie Huang, Dongdong Yu, Yaqian Li, Yandong Guo, Yuchen Guo, Liuyu Xiang, Guiguang Ding
• Abstract summary: We introduce a box-level active detection framework that controls a box-based budget per cycle. We propose Complementary Pseudo Active Strategy (ComPAS) to exploit both human annotations and the model intelligence. ComPAS consistently outperforms 10 competitors under 4 settings in a unified setting.
• Score: 47.41635810670186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection. However, the widely used active detection benchmarks conduct image-level evaluation, which is unrealistic in human workload estimation and biased towards crowded images. Furthermore, existing methods still perform image-level annotation, but equally scoring all targets within the same image incurs waste of budget and redundant labels. Having revealed above problems and limitations, we introduce a box-level active detection framework that controls a box-based budget per cycle, prioritizes informative targets and avoids redundancy for fair comparison and efficient application. Under the proposed box-level setting, we devise a novel pipeline, namely Complementary Pseudo Active Strategy (ComPAS). It exploits both human annotations and the model intelligence in a complementary fashion: an efficient input-end committee queries labels for informative objects only; meantime well-learned targets are identified by the model and compensated with pseudo-labels. ComPAS consistently outperforms 10 competitors under 4 settings in a unified codebase. With supervision from labeled data only, it achieves 100% supervised performance of VOC0712 with merely 19% box annotations. On the COCO dataset, it yields up to 4.3% mAP improvement over the second-best method. ComPAS also supports training with the unlabeled pool, where it surpasses 90% COCO supervised performance with 85% label reduction. Our source code is publicly available at https://github.com/lyumengyao/blad.

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