We lifted 2 functions out of this paper's own repositories and ran 2 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 | — | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| conv_ws | Ran | this paper's copy was not recorded; identical code first harvested from adonis-galaxy/depthclip pointer only · get_code("06873a4d1c461f5f") |
| mish | Ran | this paper's copy was not recorded; identical code first harvested from adonis-galaxy/depthclip pointer only · get_code("b3fc125f523d185a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edge detection. Based on the Swin transformer model, our framework encodes the input image into a shared representation and makes predictions for each vision task using task-specific transformer-based decoder heads. At the heart of our approach is a shared attention mechanism modeling the dependencies across the tasks. We evaluate our model on several multitask benchmarks, showing that our MulT framework outperforms both the state-of-the art multitask convolutional neural network models and all the respective single task transformer models. Our experiments further highlight the benefits of sharing attention across all the tasks, and demonstrate that our MulT model is robust and generalizes well to new domains. Our project website is at https://ivrl.github.io/MulT/.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2205.08303")
get_code_for_paper("2205.08303")
have("2205.08303")
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