Jinyoung Park, Minseok Son, Seungju Cho, Inyoung Lee, Changick Kim
We lifted 8 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| jinyxp/weather4cast-2022 | alias | 6 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| DownConv | Ran | jinyxp/weather4cast-2022/models/RainUNET.py code served (permissive licence) · get_code("5c04b46d301f8ac2") |
| GridAttention | Ran | jinyxp/weather4cast-2022/models/RainUNET.py code served (permissive licence) · get_code("a6730c7058b85eca") |
| ResizeConv | Ran | jinyxp/weather4cast-2022/models/RainUNET.py code served (permissive licence) · get_code("514c7c0584d7ee25") |
| autocrop | Ran | jinyxp/weather4cast-2022/models/RainUNET.py code served (permissive licence) · get_code("9288d9869f40857f") |
| get_activation | Ran | jinyxp/weather4cast-2022/models/RainUNET.py code served (permissive licence) · get_code("689e68cf031b9f81") |
| upconv2 | Ran | jinyxp/weather4cast-2022/models/RainUNET.py code served (permissive licence) · get_code("03d8a887bb4900ac") |
| UNet | Not yet run | jinyxp/weather4cast-2022/models/RainUNET.py code served (permissive licence) · get_code("dbdf5a7a803888ee") |
| UpConv | Not yet run | jinyxp/weather4cast-2022/models/RainUNET.py code served (permissive licence) · get_code("0e0c16f47f48ed39") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper presents a solution to the Weather4cast 2022 Challenge Stage 2. The goal of the challenge is to forecast future high-resolution rainfall events obtained from ground radar using low-resolution multiband satellite images. We suggest a solution that performs data preprocessing appropriate to the challenge and then predicts rainfall movies using a novel RainUNet. RainUNet is a hierarchical U-shaped network with temporal-wise separable block (TS block) using a decoupled large kernel 3D convolution to improve the prediction performance. Various evaluation metrics show that our solution is effective compared to the baseline method. The source codes are available at https://github.com/jinyxp/Weather4cast-2022.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2212.04005")
get_code_for_paper("2212.04005")
have("2212.04005")
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