Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Location-Sensitive Visual Recognition with Cross-IOU Loss   Object detection, instance segmentation, and pose estimation are popular
visual recognition tasks which require localizing the object by internal or
boundary landmarks. This paper summarizes these tasks as location-sensitive
visual recognition and proposes a unified solution named location-sensitive
network (LSNet). Based on a deep neural network as the backbone, LSNet predicts
an anchor point and a set of landmarks which together define the shape of the
target object. The key to optimizing the LSNet lies in the ability of fitting
various scales, for which we design a novel loss function named cross-IOU loss
that computes the cross-IOU of each anchor point-landmark pair to approximate
the global IOU between the prediction and ground-truth. The flexibly located
and accurately predicted landmarks also enable LSNet to incorporate richer
contextual information for visual recognition. Evaluated on the MS-COCO
dataset, LSNet set the new state-of-the-art accuracy for anchor-free object
detection (a 53.5% box AP) and instance segmentation (a 40.2% mask AP), and
shows promising performance in detecting multi-scale human poses. Code is
available at https://github.com/Duankaiwen/LSNet

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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
31
The categories of this paper are: ao-sci
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The categories of this paper are: math-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: plasm-ph
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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org