The DOI matches its Crossref record, with a small difference in title or year; 68 of the 68 references it cites were found in a registry or an index.
CCNet: Criss-Cross Attention for Semantic Segmentation
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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“Axial Attention (Huang et al., 2020; Ho et al., 2019) is a simple, yet effective technique to run selfattention on large inputs that are organized as multidimensional tensors.”
From An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (2020) · cites this paper as Huang et al., 2020
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Metadata
- 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)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2023-06-01
- Volume
- 45
- Issue
- 6
- Pages
- 6896-6908
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- 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
- 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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