SBAT: Video Captioning with Sparse Boundary-Aware Transformer

Tao Jin; Siyu Huang; Ming Chen; Yingming Li; Zhongfei Zhang

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arXiv · 2007.11888

SBAT: Video Captioning with Sparse Boundary-Aware Transformer

Tao Jin·Siyu Huang·Ming Chen·Yingming Li·Zhongfei Zhang

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Abstract

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.

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BibTeX RIS

Tao Jin, Siyu Huang, Ming Chen, Yingming Li, Zhongfei Zhang. 2020-07-23. SBAT: Video Captioning with Sparse Boundary-Aware Transformer. https://arxiv.org/abs/2007.11888

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