We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| caiyuanhao1998/MST | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| conv | Ran | caiyuanhao1998/MST/real/test_code/architecture/MST.py code served (permissive licence) · get_code("013ca1b768620edd") |
| shift_back | Ran | caiyuanhao1998/MST/real/test_code/architecture/MST.py code served (permissive licence) · get_code("d8d6ba766e739efd") |
| trunc_normal_ | Ran | caiyuanhao1998/MST/real/test_code/architecture/MST.py code served (permissive licence) · get_code("c76d18678f19d332") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system. The HSI representations are highly similar and correlated across the spectral dimension. Modeling the inter-spectra interactions is beneficial for HSI reconstruction. However, existing CNN-based methods show limitations in capturing spectral-wise similarity and long-range dependencies. Besides, the HSI information is modulated by a coded aperture (physical mask) in CASSI. Nonetheless, current algorithms have not fully explored the guidance effect of the mask for HSI restoration. In this paper, we propose a novel framework, Mask-guided Spectral-wise Transformer (MST), for HSI reconstruction. Specifically, we present a Spectral-wise Multi-head Self-Attention (S-MSA) that treats each spectral feature as a token and calculates self-attention along the spectral dimension. In addition, we customize a Mask-guided Mechanism (MM) that directs S-MSA to pay attention to spatial regions with high-fidelity spectral representations. Extensive experiments show that our MST significantly outperforms state-of-the-art (SOTA) methods on simulation and real HSI datasets while requiring dramatically cheaper computational and memory costs. Code and pre-trained models are available at https://github.com/caiyuanhao1998/MST/
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2111.07910")
get_code_for_paper("2111.07910")
have("2111.07910")
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