Alexander Ku, Jason Baldridge, Peter Anderson, Roma Patel, Eugene Ie
We lifted 4 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| VegB/Diagnose_VLN | canonical | 1 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| get_label_for_setting | Ran | VegB/Diagnose_VLN/r2r/model/Recurrent-VLN-BERT/r2r_src/utils.py code served (permissive licence) · get_code("57e01d248c7840a8") |
| compute_bleu | Not yet run | VegB/Diagnose_VLN/rxr/model/CLIP-ViL-VLN/r2r_src/bleu.py code served (permissive licence) · get_code("7b5b4685db18fbca") |
| load_nav_graphs | Not yet run | VegB/Diagnose_VLN/r2r/model/Recurrent-VLN-BERT/r2r_src/utils.py code served (permissive licence) · get_code("e3a90df1a875a38e") |
| read_tsv | Not yet run | VegB/Diagnose_VLN/rxr/model/CLIP-ViL-VLN/precomute_imagenet_views.py code served (permissive licence) · get_code("6468f9e62900b58c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset. RxR is multilingual (English, Hindi, and Telugu) and larger (more paths and instructions) than other VLN datasets. It emphasizes the role of language in VLN by addressing known biases in paths and eliciting more references to visible entities. Furthermore, each word in an instruction is time-aligned to the virtual poses of instruction creators and validators. We establish baseline scores for monolingual and multilingual settings and multitask learning when including Room-to-Room annotations (Anderson et al., 2018b). We also provide results for a model that learns from synchronized pose traces by focusing only on portions of the panorama attended to in human demonstrations. The size, scope and detail of RxR dramatically expands the frontier for research on embodied language agents in simulated, photo-realistic environments.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.07954")
get_code_for_paper("2010.07954")
have("2010.07954")
Connect an agent — have() is free.