Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Temporal Feature Warping for Video Shadow Detection   While single image shadow detection has been improving rapidly in recent
years, video shadow detection remains a challenging task due to data scarcity
and the difficulty in modelling temporal consistency. The current video shadow
detection method achieves this goal via co-attention, which mostly exploits
information that is temporally coherent but is not robust in detecting moving
shadows and small shadow regions. In this paper, we propose a simple but
powerful method to better aggregate information temporally. We use an optical
flow based warping module to align and then combine features between frames. We
apply this warping module across multiple deep-network layers to retrieve
information from neighboring frames including both local details and high-level
semantic information. We train and test our framework on the ViSha dataset.
Experimental results show that our model outperforms the state-of-the-art video
shadow detection method by 28%, reducing BER from 16.7 to 12.0.

18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
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The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg