Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Accurate One-Stage Object Detection with AP-Loss   One-stage object detectors are trained by optimizing classification-loss and
localization-loss simultaneously, with the former suffering much from extreme
foreground-background class imbalance issue due to the large number of anchors.
This paper alleviates this issue by proposing a novel framework to replace the
classification task in one-stage detectors with a ranking task, and adopting
the Average-Precision loss (AP-loss) for the ranking problem. Due to its
non-differentiability and non-convexity, the AP-loss cannot be optimized
directly. For this purpose, we develop a novel optimization algorithm, which
seamlessly combines the error-driven update scheme in perceptron learning and
backpropagation algorithm in deep networks. We verify good convergence property
of the proposed algorithm theoretically and empirically. Experimental results
demonstrate notable performance improvement in state-of-the-art one-stage
detectors based on AP-loss over different kinds of classification-losses on
various benchmarks, without changing the network architectures. Code is
available at https://github.com/cccorn/AP-loss.

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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: stat
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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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: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: patt-sol