Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Locating Objects Without Bounding Boxes   Recent advances in convolutional neural networks (CNN) have achieved
remarkable results in locating objects in images. In these networks, the
training procedure usually requires providing bounding boxes or the maximum
number of expected objects. In this paper, we address the task of estimating
object locations without annotated bounding boxes which are typically
hand-drawn and time consuming to label. We propose a loss function that can be
used in any fully convolutional network (FCN) to estimate object locations.
This loss function is a modification of the average Hausdorff distance between
two unordered sets of points. The proposed method has no notion of bounding
boxes, region proposals, or sliding windows. We evaluate our method with three
datasets designed to locate people's heads, pupil centers and plant centers. We
outperform state-of-the-art generic object detectors and methods fine-tuned for
pupil tracking.

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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: chem-ph
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The categories of this paper are: econ
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The categories of this paper are: eess
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol