We lifted 13 functions out of this paper's own repositories and ran 11 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 |
|---|---|---|
| pgodet/star_flow | canonical | 11 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| apply_transform_to_params | Ran | pgodet/star_flow/augmentations.py code served (permissive licence) · get_code("fc31c500a57e244d") |
| compute_color | Ran | pgodet/star_flow/utils/flow.py code served (permissive licence) · get_code("dc5b20ec12d3ae87") |
| conv | Ran | pgodet/star_flow/models/irr_modules.py code served (permissive licence) · get_code("9db5fbaedd8ed8b4") |
| denormalize_coords | Ran | pgodet/star_flow/augmentations.py code served (permissive licence) · get_code("49af4e3fbd112a56") |
| f1_score_bal_loss | Ran | pgodet/star_flow/losses.py code served (permissive licence) · get_code("e90ca7d9eaedb594") |
| flow_to_png | Ran | pgodet/star_flow/utils/flow.py code served (permissive licence) · get_code("c39cf1b0372a9391") |
| flow_to_png_middlebury | Ran | pgodet/star_flow/utils/flow.py code served (permissive licence) · get_code("3b3ea1e0c955f8fe") |
| get_default_logging_format | Ran | pgodet/star_flow/logger.py code served (permissive licence) · get_code("6eaba3d6df46f7ed") |
| normalize_coords | Ran | pgodet/star_flow/augmentations.py code served (permissive licence) · get_code("c92c8a169968453b") |
| subtract_mean | Ran | pgodet/star_flow/models/irr_modules.py code served (permissive licence) · get_code("538f9abf58b6c890") |
| upsample_factor2 | Ran | pgodet/star_flow/models/irr_modules.py code served (permissive licence) · get_code("606dfd0419b77fe8") |
| f1_score | Not yet run | pgodet/star_flow/losses.py code served (permissive licence) · get_code("e351a73a9141e59d") |
| fbeta_score | Not yet run | pgodet/star_flow/losses.py code served (permissive licence) · get_code("0185c542a4ae3ac4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a new lightweight CNN-based algorithm for multi-frame optical flow estimation. Our solution introduces a double recurrence over spatial scale and time through repeated use of a generic "STaR" (SpatioTemporal Recurrent) cell. It includes (i) a temporal recurrence based on conveying learned features rather than optical flow estimates; (ii) an occlusion detection process which is coupled with optical flow estimation and therefore uses a very limited number of extra parameters. The resulting STaRFlow algorithm gives state-of-the-art performances on MPI Sintel and Kitti2015 and involves significantly less parameters than all other methods with comparable results.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2007.05481")
get_code_for_paper("2007.05481")
have("2007.05481")
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