The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
Visual attention network
Open accessAbstract
While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision: (1) treating images as 1D sequences neglects their 2D structures; (2) the quadratic complexity is too expensive for high-resolution images; (3) it only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings. Furthermore, we present a neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple, VAN achieves comparable results with similar size convolutional neural networks (CNNs) and vision transformers (ViTs) in various tasks, including image classification, object detection, semantic segmentation, panoptic segmentation, pose estimation, etc. For example, VAN-B6 achieves 87.8% accuracy on ImageNet benchmark, and sets new state-of-the-art performance (58.2 PQ) for panoptic segmentation. Besides, VAN-B2 surpasses Swin-T 4 mIoU (50.1 vs. 46.1) for semantic segmentation on ADE20K benchmark, 2.6 AP (48.8 vs. 46.2) for object detection on COCO dataset. It provides a novel method and a simple yet strong baseline for the community. The code is available at https://github.com/Visual-Attention-Network .
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Cites
Deep Residual Learning for Image Recognition
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Densely Connected Convolutional Networks
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Self-citationGoing deeper with convolutions
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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Large Selective Kernel Network for Remote Sensing Object Detection
Li, Y., Hou, Q., Zheng, Z., Cheng, M.-M., Yang, J., & Li, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01540 · Cited by 893
Self-citationInceptionNeXt: When Inception Meets ConvNeXt
Yu, W., Zhou, P., Yan, S., & Wang, X. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00542 · Cited by 423
Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 389
MetaFormer Baselines for Vision
Yu, W., Si, C., Zhou, P., Luo, M., Zhou, Y., Feng, J., Yan, S., & Wang, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3329173 · Cited by 331
NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results
Li, Y., Zhang, Y., Timofte, R., Van Gool, L., Yu, L., Li, Y., Li, X., Jiang, T., Wu, Q., Han, M., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00189 · Cited by 72
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Metadata
- Authors
- Guo, M.-H., Lu, C.-Z., Liu, Z.-N., Cheng, M.-M., & Hu, S.-M.
- Venue
- Computational Visual Media
- Publisher
- Tsinghua University Press
- Published
- 2023-12
- Volume
- 9
- Issue
- 4
- Pages
- 733-752
- ISSN
- 2096-0433 (ISSN-L)
- 2096-0662
- OpenAlex
- W4385346076
- Cited by
- 1,073
- References
- 96
- FWCI
- 94.65
- Type
- journal article
- Language
- en
- Open access
- diamond
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