Learning Object Scale With Click Supervision for Object Detection

• URL: http://arxiv.org/abs/2002.08555v1
• Date: Thu, 20 Feb 2020 03:59:46 GMT
• Title: Learning Object Scale With Click Supervision for Object Detection
• Authors: Liao Zhang, Yan Yan, Lin Cheng, and Hanzi Wang
• Abstract summary: We propose a simple yet effective method which incorporatesCNN visualization with click supervision to generate the pseudoground-truths. These pseudo ground-truthscans be used to train a fully-supervised detector. Experimental results on the PASCAL VOC2007 and VOC 2012 datasets show that the proposed methodcan obtain much higher accuracy for estimating the object scale.
• Score: 29.421113887739413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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