Papers › Exploiting Web Images for Weakly Supervised Object Detection

Exploiting Web Images for Weakly Supervised Object Detection

27 Jul 2017arXiv:1707.08721archive 2025-07-28

Qingyi Tao, Hao Yang, Jianfei Cai

In recent years, the performance of object detection has advanced significantly with the evolving deep convolutional neural networks. However, the state-of-the-art object detection methods still rely on accurate bounding box annotations that require extensive human labelling. Object detection without bounding box annotations, i.e, weakly supervised detection methods, are still lagging far behind. As weakly supervised detection only uses image level labels and does not require the ground truth of bounding box location and label of each object in an image, it is generally very difficult to distill knowledge of the actual appearances of objects. Inspired by curriculum learning, this paper proposes an easy-to-hard knowledge transfer scheme that incorporates easy web images to provide prior knowledge of object appearance as a good starting point. While exploiting large-scale free web imagery, we introduce a sophisticated labour free method to construct a web dataset with good diversity in object appearance. After that, semantic relevance and distribution relevance are introduced and utilized in the proposed curriculum training scheme. Our end-to-end learning with the constructed web data achieves remarkable improvement across most object classes especially for the classes that are often considered hard in other works.

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Tasks

Diversity Object Object Detection Transfer Learning Weakly Supervised Object Detection object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Weakly Supervised Object Detection	PASCAL VOC 2007	WebRelETH	MAP	46.0	#30 of 41	Archive leaderboard	report
Weakly Supervised Object Detection	PASCAL VOC 2012 test	WebRelETH	MAP	42.8	#23 of 32	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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