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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 2: AAAI-22 Technical Tracks 2

Fully Attentional Network for Semantic Segmentation

February 1, 2023

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Authors

Qi Song

The Chinese University of Hong Kong, Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society


Jie Li

The Chinese University of Hong Kong, Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society


Chenghong Li

The Chinese University of Hong Kong, Shenzhen


Hao Guo

The Chinese University of Hong Kong, Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society


Rui Huang

The Chinese University of Hong Kong, Shenzhen


DOI:

10.1609/aaai.v36i2.20126


Abstract:

Recent non-local self-attention methods have proven to be effective in capturing long-range dependencies for semantic segmentation. These methods usually form a similarity map of R^(CxC) (by compressing spatial dimensions) or R^(HWxHW) (by compressing channels) to describe the feature relations along either channel or spatial dimensions, where C is the number of channels, H and W are the spatial dimensions of the input feature map. However, such practices tend to condense feature dependencies along the other dimensions, hence causing attention missing, which might lead to inferior results for small/thin categories or inconsistent segmentation inside large objects. To address this problem, we propose a new approach, namely Fully Attentional Network (FLANet), to encode both spatial and channel attentions in a single similarity map while maintaining high computational efficiency. Specifically, for each channel map, our FLANet can harvest feature responses from all other channel maps, and the associated spatial positions as well, through a novel fully attentional module. Our new method has achieved state-of-the-art performance on three challenging semantic segmentation datasets, i.e., 83.6%, 46.99%, and 88.5% on the Cityscapes test set, the ADE20K validation set, and the PASCAL VOC test set, respectively.

Topics: AAAI

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HOW TO CITE:

Qi Song||Jie Li||Chenghong Li||Hao Guo||Rui Huang Fully Attentional Network for Semantic Segmentation Proceedings of the AAAI Conference on Artificial Intelligence (2022) 2280-2288.

Qi Song||Jie Li||Chenghong Li||Hao Guo||Rui Huang Fully Attentional Network for Semantic Segmentation AAAI 2022, 2280-2288.

Qi Song||Jie Li||Chenghong Li||Hao Guo||Rui Huang (2022). Fully Attentional Network for Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 2280-2288.

Qi Song||Jie Li||Chenghong Li||Hao Guo||Rui Huang. Fully Attentional Network for Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.2280-2288.

Qi Song||Jie Li||Chenghong Li||Hao Guo||Rui Huang. 2022. Fully Attentional Network for Semantic Segmentation. "Proceedings of the AAAI Conference on Artificial Intelligence". 2280-2288.

Qi Song||Jie Li||Chenghong Li||Hao Guo||Rui Huang. (2022) "Fully Attentional Network for Semantic Segmentation", Proceedings of the AAAI Conference on Artificial Intelligence, p.2280-2288

Qi Song||Jie Li||Chenghong Li||Hao Guo||Rui Huang, "Fully Attentional Network for Semantic Segmentation", AAAI, p.2280-2288, 2022.

Qi Song||Jie Li||Chenghong Li||Hao Guo||Rui Huang. "Fully Attentional Network for Semantic Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.2280-2288.

Qi Song||Jie Li||Chenghong Li||Hao Guo||Rui Huang. "Fully Attentional Network for Semantic Segmentation". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 2280-2288.

Qi Song||Jie Li||Chenghong Li||Hao Guo||Rui Huang. Fully Attentional Network for Semantic Segmentation. AAAI[Internet]. 2022[cited 2023]; 2280-2288.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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