We lifted 12 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| ppliuboy/SelFlow | canonical | 7 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| config_dict | Ran | ppliuboy/SelFlow/config/extract_config.py code served (permissive licence) · get_code("3dd5c269c8110162") |
| flow_horizontal_flip | Ran | ppliuboy/SelFlow/data_augmentation.py code served (permissive licence) · get_code("177a030ea67ee72e") |
| flow_vertical_flip | Ran | ppliuboy/SelFlow/data_augmentation.py code served (permissive licence) · get_code("a8dc427be3802afc") |
| lrelu | Ran | ppliuboy/SelFlow/utils.py code served (permissive licence) · get_code("69e0961ea54f416c") |
| read_pfm | Ran | ppliuboy/SelFlow/flowlib.py code served (permissive licence) · get_code("f8c75b69f9e7a932") |
| rgb_bgr | Ran | ppliuboy/SelFlow/utils.py code served (permissive licence) · get_code("7a1e9253c132b589") |
| tf_warp | Ran | ppliuboy/SelFlow/warp.py code served (permissive licence) · get_code("34af5f8b54be1bf2") |
| flow_to_color | Not yet run | ppliuboy/SelFlow/flowlib.py code served (permissive licence) · get_code("3208c90e2942acfa") |
| get_pixel_value | Not yet run | ppliuboy/SelFlow/warp.py code served (permissive licence) · get_code("76d9ba255fb4180c") |
| mvn | Not yet run | ppliuboy/SelFlow/utils.py code served (permissive licence) · get_code("167f43828fef8e2f") |
| random_crop | Not yet run | ppliuboy/SelFlow/data_augmentation.py code served (permissive licence) · get_code("2265da3e272fe1dd") |
| read_flo | Not yet run | ppliuboy/SelFlow/flowlib.py code served (permissive licence) · get_code("63f11096bd0174c0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a self-supervised learning approach for optical flow. Our method distills reliable flow estimations from non-occluded pixels, and uses these predictions as ground truth to learn optical flow for hallucinated occlusions. We further design a simple CNN to utilize temporal information from multiple frames for better flow estimation. These two principles lead to an approach that yields the best performance for unsupervised optical flow learning on the challenging benchmarks including MPI Sintel, KITTI 2012 and 2015. More notably, our self-supervised pre-trained model provides an excellent initialization for supervised fine-tuning. Our fine-tuned models achieve state-of-the-art results on all three datasets. At the time of writing, we achieve EPE=4.26 on the Sintel benchmark, outperforming all submitted methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1904.09117")
get_code_for_paper("1904.09117")
have("1904.09117")
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