We lifted 15 functions out of this paper's own repositories and ran 9 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 |
|---|---|---|
| ego4d/hands-and-objects | canonical | 1 of 1 |
| ego4d/episodic-memory | pwc_unofficial | 5 of 6 |
| copy not recorded | — | 2 of 2 |
| ego4d/forecasting | pwc_unofficial | 1 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| bi_loss | Ran | ego4d/episodic-memory/MQ/Models/Loss.py code served (permissive licence) · get_code("7ec8e5ea9abf1846") |
| convert_annotations_to_clipwise_list | Ran | ego4d/episodic-memory/VQ2D/extract_vq_detection_scores.py code served (permissive licence) · get_code("ca522943b8eb15aa") |
| get_loss_supplement | Ran | ego4d/episodic-memory/MQ/Models/Loss.py code served (permissive licence) · get_code("05a34cc547463ca5") |
| get_neigh_idx_semantic | Ran | ego4d/episodic-memory/MQ/Models/GCNs.py code served (permissive licence) · get_code("b00a3a1790c6e560") |
| interpolated_prec_rec | Ran | this paper's copy was not recorded; identical code first harvested from Ontheway361/BSN_pytorch pointer only · get_code("81aab88321de62c2") |
| nms | Ran | ego4d/episodic-memory/MQ/Infer.py code served (permissive licence) · get_code("2ab50acb3969a597") |
| round_width | Ran | ego4d/forecasting/ego4d_forecasting/models/video_model_builder.py code served (permissive licence) · get_code("b07e3607b90c1e7c") |
| segment_iou | Ran | this paper's copy was not recorded; identical code first harvested from Ontheway361/BSN_pytorch pointer only · get_code("d0744a2fe3151508") |
| wrapper_segment_iou | Ran | ego4d/hands-and-objects/state-change-localization-classification/bmn/BMN-Boundary-Matching-Network/Evaluation/eval_proposal.py code served (permissive licence) · get_code("265572629e36af77") |
| drop_path | Not yet run | ego4d/forecasting/ego4d_forecasting/models/video_model_builder.py code served (permissive licence) · get_code("bcc1cdae3bb3212c") |
| get_loss_func | Not yet run | ego4d/forecasting/ego4d_forecasting/models/losses.py code served (permissive licence) · get_code("dd72775d1ea00db7") |
| get_norm | Not yet run | ego4d/forecasting/ego4d_forecasting/models/batchnorm_helper.py code served (permissive licence) · get_code("952f23f7aff7ce7d") |
| get_trans_func | Not yet run | ego4d/forecasting/ego4d_forecasting/models/resnet_helper.py code served (permissive licence) · get_code("4d6991202a8d6097") |
| is_detection_enabled | Not yet run | ego4d/forecasting/ego4d_forecasting/models/video_model_builder.py code served (permissive licence) · get_code("a99430fceaa1dc2d") |
| knn | Not yet run | ego4d/episodic-memory/MQ/Models/GCNs.py code served (permissive licence) · get_code("9a2621658570ef96") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.07058")
get_code_for_paper("2110.07058")
have("2110.07058")
Connect an agent — have() is free.