An attention based method for video semantic segmentation

Yuan Huang; Qian Huang; Shuai Huang; Yanping Li

doi:10.1117/12.2573258

12 June 2020 An attention based method for video semantic segmentation

Yuan Huang, Qian Huang, Shuai Huang, Yanping Li

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115190Q (2020) https://doi.org/10.1117/12.2573258
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

OSVOS is one of the best algorithms in video semantic segmentation of single-target. CBAM module is proposed in object detection and classification. This module can improve the performance of the network without adding extra computation. In view of this, this paper proposes an end-to-end network AttnVSS based on attention and VGG-16. CBAM is embedded in the shallow layer of our network. The network is first pre-trained on ImageNet, and then full connection layer is removed and fine-tuned to meet the segmentation requirements. Through the ablation experiment on DAVIS dataset, it is proved that CBAM can be embedded into semantic segmentation networks. At the same time, it can help the network to allocate "attention", focus on the most meaningful part of the input, to achieve faster and more accurate segmentation.

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Yuan Huang, Qian Huang, Shuai Huang, and Yanping Li "An attention based method for video semantic segmentation", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115190Q (12 June 2020); https://doi.org/10.1117/12.2573258

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KEYWORDS

Image segmentation

Video

Semantic video

Convolution

Classification systems

Data modeling

Visualization

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