Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DCCO: Towards Deformable Continuous Convolution Operators   Discriminative Correlation Filter (DCF) based methods have shown competitive
performance on tracking benchmarks in recent years. Generally, DCF based
trackers learn a rigid appearance model of the target. However, this reliance
on a single rigid appearance model is insufficient in situations where the
target undergoes non-rigid transformations. In this paper, we propose a unified
formulation for learning a deformable convolution filter. In our framework, the
deformable filter is represented as a linear combination of sub-filters. Both
the sub-filter coefficients and their relative locations are inferred jointly
in our formulation. Experiments are performed on three challenging tracking
benchmarks: OTB- 2015, TempleColor and VOT2016. Our approach improves the
baseline method, leading to performance comparable to state-of-the-art.

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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin