Cited by 526Advanced Neural Network ApplicationsVisual Attention and Saliency DetectionDomain Adaptation and Few-Shot Learning

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CCNet: Criss-Cross Attention for Semantic Segmentation

Huang, Zilong, Wang, Xinggang, Wei, Yunchao, Huang, Lichao, Shi, Humphrey, Liu, Wenyu, Huang, Thomas S.·2023·IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI 10.1109/tpami.2020.3007032

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Abstract

Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a criss-cross network (CCNet) for obtaining full-image contextual information in a very effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. Besides, a category consistent loss is proposed to enforce the criss-cross attention module to produce more discriminative features. Overall, CCNet is with the following merits: 1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11× less GPU memory usage. 2) High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85 percent of the non-local block. 3) The state-of-the-art performance. We conduct extensive experiments on semantic segmentation benchmarks including Cityscapes, ADE20K, human parsing benchmark LIP, instance segmentation benchmark COCO, video segmentation benchmark CamVid. In particular, our CCNet achieves the mIoU scores of 81.9, 45.76 and 55.47 percent on the Cityscapes test set, the ADE20K validation set and the LIP validation set respectively, which are the new state-of-the-art results. The source codes are available at https://github.com/speedinghzl/CCNethttps://github.com/speedinghzl/CCNet.

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Authors: Huang, Z., Wang, X., Wei, Y., Huang, L., Shi, H., Liu, W., & Huang, T. S.
Affiliations: Huazhong University of Science and Technology (CN)
University of Technology Sydney (AU)
Horizon Robotics (China) (CN)
University of Illinois Urbana-Champaign (US)
ORCID: Huang, Zilong
Wang, Xinggang
Wei, Yunchao
Huang, Lichao
Shi, Humphrey
Liu, Wenyu
Huang, Thomas S.
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2023-06-01
Volume: 45
Issue: 6
Pages: 6896-6908
ISSN: 0162-8828 (ISSN-L)
2160-9292
1939-3539
DOI: 10.1109/tpami.2020.3007032
OpenAlex: W3040304705
Cited by: 524
References: 66
Citations by year: 2026: 35 · 2025: 102 · 2024: 110 · 2023: 83
FWCI: 26.73
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (61733007, 61876212)
ARC DECRA (DE190101315, ARC DP200100938)
HUST-Horizon Computer Vision Research Center
Center for Cognitive Computing Systems Research
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: criss-cross attention, semantic segmentation, contextual information, recurrent attention, GPU memory efficiency, computational efficiency, non-local block, instance segmentation, video segmentation, human parsing

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CCNet: Criss-Cross Attention for Semantic Segmentation

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