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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 3: AAAI-21 Technical Tracks 3

AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing

February 1, 2023

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Authors

Qi Song

Shenzhen Institute of Artificial Intelligence and Robotics for Society Jilin University


Kangfu Mei

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 Shenzhen Institute of Artificial Intelligence and Robotics for Society


DOI:

10.1609/aaai.v35i3.16359


Abstract:

Two factors have proven to be very important to the performance of semantic segmentation models: global context and multi-level semantics. However, generating features that capture both factors always leads to high computational complexity, which is problematic in real-time scenarios. In this paper, we propose a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multi-level semantics while keeping the efficiency high. AttaNet consists of two primary modules: Strip Attention Module (SAM) and Attention Fusion Module (AFM). Viewing that in challenging images with low segmentation accuracy, there are a significantly larger amount of vertical strip areas than horizontal ones, SAM utilizes a striping operation to reduce the complexity of encoding global context in the vertical direction drastically while keeping most of contextual information, compared to the non-local approaches. Moreover, AFM follows a cross-level aggregation strategy to limit the computation, and adopts an attention strategy to weight the importance of different levels of features at each pixel when fusing them, obtaining an efficient multi-level representation. We have conducted extensive experiments on two semantic segmentation benchmarks, and our network achieves different levels of speed/accuracy trade-offs on Cityscapes, e.g., 71 FPS/79.9% mIoU, 130 FPS/78.5% mIoU, and 180 FPS/70.1% mIoU, and leading performance on ADE20K as well.

Topics: AAAI

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HOW TO CITE:

Qi Song||Kangfu Mei||Rui Huang AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing Proceedings of the AAAI Conference on Artificial Intelligence (2021) 2567-2575.

Qi Song||Kangfu Mei||Rui Huang AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing AAAI 2021, 2567-2575.

Qi Song||Kangfu Mei||Rui Huang (2021). AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing. Proceedings of the AAAI Conference on Artificial Intelligence, 2567-2575.

Qi Song||Kangfu Mei||Rui Huang. AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.2567-2575.

Qi Song||Kangfu Mei||Rui Huang. 2021. AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing. "Proceedings of the AAAI Conference on Artificial Intelligence". 2567-2575.

Qi Song||Kangfu Mei||Rui Huang. (2021) "AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing", Proceedings of the AAAI Conference on Artificial Intelligence, p.2567-2575

Qi Song||Kangfu Mei||Rui Huang, "AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing", AAAI, p.2567-2575, 2021.

Qi Song||Kangfu Mei||Rui Huang. "AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.2567-2575.

Qi Song||Kangfu Mei||Rui Huang. "AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 2567-2575.

Qi Song||Kangfu Mei||Rui Huang. AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing. AAAI[Internet]. 2021[cited 2023]; 2567-2575.


ISSN: 2374-3468


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