The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection
Open accessAbstract
Convolutional neural networks (CNNs) are good at extracting contexture features within certain receptive fields, while transformers can model the global long-range dependency features. By absorbing the advantage of transformer and the merit of CNN, Swin Transformer shows strong feature representation ability. Based on it, we propose a cross-modality fusion model,SwinNet, for RGB-D and RGB-T salient object detection. It is driven by Swin Transformer to extract the hierarchical features, boosted by attention mechanism to bridge the gap between two modalities, and guided by edge information to sharp the contour of salient object. To be specific, two-stream Swin Transformer encoder first extracts multi-modality features, and then spatial alignment and channel re-calibration module is presented to optimize intra-level cross-modality features. To clarify the fuzzy boundary, edge-guided decoder achieves inter-level cross-modality fusion under the guidance of edge features. The proposed model outperforms the state-of-the-art models on RGB-D and RGB-T datasets, showing that it provides more insight into the cross-modality complementarity task.
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Cites
Deep Residual Learning for Image Recognition
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
Self-citationA Computational Approach to Edge Detection
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Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259 · cited 3×
“1) The Effectiveness of Swin Transformer Backbone: We replaces Swin Transformer backbone with some CNN backbones (e.g., ResNet-50 [96], Res2Net-50 [97], ResNet-101 [96], Res50+ViT16 [98]) and transformer backbones (e.g., T2T-14 [66] and PVT-M [68]) to check the effectiveness of backbones.”
BackgroundTransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021) · arXiv · doi:10.48550/arxiv.2102.04306 · Cited by 4,206 · cited 1×
“, ResNet-50[96], Res2Net-50[97], ResNet-101[96], Res50+ViT16[98]) and transformer backbones (e.”
Frequency-tuned salient region detection
Achanta, R., Hemami, S., Estrada, F., & Susstrunk, S. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206596 · Cited by 4,167
Res2Net: A New Multi-Scale Backbone Architecture
Gao, S.-H., Cheng, M.-M., Zhao, K., Zhang, X.-Y., Yang, M.-H., & Torr, P. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2938758 · Cited by 3,385
Cited by
Hussain, M. (2023) · Machines · doi:10.3390/machines11070677 · Cited by 1,217
Metadata
- Authors
- Liu, Z., Tan, Y., He, Q., & Xiao, Y.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2022-07
- Volume
- 32
- Issue
- 7
- Pages
- 4486-4497
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W3212645988
- Cited by
- 384
- References
- 96
- FWCI
- 20.46
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection. https://app.cochat.ai/papers/10.1109/tcsvt.2021.3127149
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- Liu, Z., Tan, Y., He, Q., & Xiao, Y. (2022). SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection. IEEE Transactions on Circuits and Systems for Video Technology. https://doi.org/10.1109/tcsvt.2021.3127149
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