We lifted 4 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| rinuboney/fpac | canonical | 0 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| compute_heatmaps | Not yet run | rinuboney/fpac/keypoints_encoder.py code served (permissive licence) · get_code("979a04c1a1e13358") |
| compute_keypoints_1d | Not yet run | rinuboney/fpac/keypoints_encoder.py code served (permissive licence) · get_code("6df9e012797c56a6") |
| compute_pixel_offsets | Not yet run | rinuboney/fpac/dm_wrappers.py code served (permissive licence) · get_code("1329feb98629ca3f") |
| xyz2pixels | Not yet run | rinuboney/fpac/dm_wrappers.py code served (permissive licence) · get_code("3e01094de0235cb4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In many control problems that include vision, optimal controls can be inferred from the location of the objects in the scene. This information can be represented using feature points, which is a list of spatial locations in learned feature maps of an input image. Previous works show that feature points learned using unsupervised pre-training or human supervision can provide good features for control tasks. In this paper, we show that it is possible to learn efficient feature point representations end-to-end, without the need for unsupervised pre-training, decoders, or additional losses. Our proposed architecture consists of a differentiable feature point extractor that feeds the coordinates of the estimated feature points directly to a soft actor-critic agent. The proposed algorithm yields performance competitive to the state-of-the art on DeepMind Control Suite tasks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.07995")
get_code_for_paper("2106.07995")
have("2106.07995")
Connect an agent — have() is free.