The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
Open accessAbstract
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI). These CNN-based methods achieve impressive restoration performance while showing limitations in capturing the long-range dependencies and self-similarity prior. To cope with this problem, we propose a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++), for efficient spectral reconstruction. In particular, we employ Spectral-wise Multi-head Self-attention (S-MSA) that is based on the HSI spatially sparse while spectrally self-similar nature to compose the basic unit, Spectral-wise Attention Block (SAB). Then SABs build up Single-stage Spectral-wise Transformer (SST) that exploits a U-shaped structure to extract multi-resolution contextual information. Finally, our MST++, cascaded by several SSTs, progressively improves the reconstruction quality from coarse to fine. Comprehensive experiments show that our MST++ significantly outperforms other state-of-the-art methods. In the NTIRE 2022 Spectral Reconstruction Challenge, our approach won the First place. Code and pre-trained models are publicly available at https://github.com/caiyuanhao1998/MST-plus-plus.
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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End-to-End Object Detection with Transformers
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End-to-End Object Detection with Transformers
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Enhanced Deep Residual Networks for Single Image Super-Resolution
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“Meanwhile, sampling redundant key elements may easily lead to over-smooth results [41] and even non-convergence issue [93].”
BackgroundMethodSwin-Unet: Unet-like pure transformer for medical image segmentation
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SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
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SwinIR: Image Restoration Using Swin Transformer
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Self-citation
Metadata
- Authors
- Cai, Y., Lin, J., Lin, Z., Wang, H., Zhang, Y., Pfister, H., Timofte, R., & Gool, L. V.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 744-754
- OpenAlex
- W4224294196
- Cited by
- 334
- References
- 81
- FWCI
- 25.69
- Type
- conference paper
- Language
- en
- Open access
- green
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