Learning Object Scale With Click Supervision for Object Detection

Liao Zhang; Yan Yan; Lin Cheng; Hanzi Wang

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arXiv · 2002.08555

Learning Object Scale With Click Supervision for Object Detection

Liao Zhang·Yan Yan·Lin Cheng·Hanzi Wang

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Abstract

Weakly-supervised object detection has recently attracted increasing attention since it only requires image-levelannotations. However, the performance obtained by existingmethods is still far from being satisfactory compared with fully-supervised object detection methods. To achieve a good trade-off between annotation cost and object detection performance,we propose a simple yet effective method which incorporatesCNN visualization with click supervision to generate the pseudoground-truths (i.e., bounding boxes). These pseudo ground-truthscan be used to train a fully-supervised detector. To estimatethe object scale, we firstly adopt a proposal selection algorithmto preserve high-quality proposals, and then generate ClassActivation Maps (CAMs) for these preserved proposals by theproposed CNN visualization algorithm called Spatial AttentionCAM. Finally, we fuse these CAMs together to generate pseudoground-truths and train a fully-supervised object detector withthese ground-truths. Experimental results on the PASCAL VOC2007 and VOC 2012 datasets show that the proposed methodcan obtain much higher accuracy for estimating the object scale,compared with the state-of-the-art image-level based methodsand the center-click based method

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BibTeX RIS

Liao Zhang, Yan Yan, Lin Cheng, Hanzi Wang. 2020-02-20. Learning Object Scale With Click Supervision for Object Detection. https://arxiv.org/abs/2002.08555

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