Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Object Scale With Click Supervision for Object Detection   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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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th