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 |
|---|---|---|
| copy not recorded | — | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| conv | Ran | this paper's copy was not recorded; identical code first harvested from caiyuanhao1998/MST pointer only · get_code("013ca1b768620edd") |
| shift_back | Ran | this paper's copy was not recorded; identical code first harvested from caiyuanhao1998/MST pointer only · get_code("d8d6ba766e739efd") |
| trunc_normal_ | Ran | this paper's copy was not recorded; identical code first harvested from caiyuanhao1998/MST pointer only · get_code("c76d18678f19d332") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2204.07908")
get_code_for_paper("2204.07908")
have("2204.07908")
Connect an agent — have() is free.