Richard Hartley, Hongdong Li, Dylan Campbell, Yao Lu, Shihao Jiang
We lifted 4 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| open-mmlab/mmflow | pwc_unofficial | 3 of 3 |
| zacjiang/GMA | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| Aggregate | Ran | zacjiang/GMA/core/gma.py pointer only (licence: WTFPL) · get_code("421e324f0d36348f") |
| end_point_error | Ran | open-mmlab/mmflow/mmflow/core/evaluation/metrics.py code served (permissive licence) · get_code("da54ea98547c05a0") |
| end_point_error_map | Ran | open-mmlab/mmflow/mmflow/core/evaluation/metrics.py code served (permissive licence) · get_code("986a031d5cc8821b") |
| optical_flow_outliers | Ran | open-mmlab/mmflow/mmflow/core/evaluation/metrics.py code served (permissive licence) · get_code("460ed36a715ed976") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Figure 1. Global motion aggregation helps resolve ambiguities caused by occlusions. Occlusions-a term we extend to include any parts of a scene that disappear in the next frame-cause large ambiguities in the optical flow estimation problem that cannot be resolved by local approaches. Based on the assumption that points on an object have homogeneous motions, which often holds approximately, we propose to globally aggregate motion features of pixels that are likely to belong to the same object. In this example, most pixels on the blade move out-of-frame from frame 2 to frame 3. When only these two frames are provided, global aggregation allows motion information to be passed from non-occluded pixels to occluded pixels, which helps resolve ambiguities caused by occlusions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2104.02409")
get_code_for_paper("2104.02409")
have("2104.02409")
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