Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SBAT: Video Captioning with Sparse Boundary-Aware Transformer   In this paper, we focus on the problem of applying the transformer structure
to video captioning effectively. The vanilla transformer is proposed for
uni-modal language generation task such as machine translation. However, video
captioning is a multimodal learning problem, and the video features have much
redundancy between different time steps. Based on these concerns, we propose a
novel method called sparse boundary-aware transformer (SBAT) to reduce the
redundancy in video representation. SBAT employs boundary-aware pooling
operation for scores from multihead attention and selects diverse features from
different scenarios. Also, SBAT includes a local correlation scheme to
compensate for the local information loss brought by sparse operation. Based on
SBAT, we further propose an aligned cross-modal encoding scheme to boost the
multimodal interaction. Experimental results on two benchmark datasets show
that SBAT outperforms the state-of-the-art methods under most of the metrics.

11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
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The categories of this paper are: math-ph
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
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The categories of this paper are: eess
28
The categories of this paper are: alg-geom