Andrew Zisserman, Michal Valko, Jean-Baptiste Alayrac, Adrià Recasens, Jean-Bastien Grill, Florent Altché, Florian Strub, Aäron Van Den Oord, Mateusz Malinowski, Pauline Luc, Corentin Tallec, Viorica Pȃtrȃucean, and 1 more
We lifted 8 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| deepmind/brave | canonical | 7 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| compute_linearly_spaced_sample_indices | Ran | deepmind/brave/brave/datasets/sampling.py code served (permissive licence) · get_code("d72aee9f7872a1f4") |
| extend_sequence | Ran | deepmind/brave/brave/datasets/media_sequences.py code served (permissive licence) · get_code("4e1f5f77cae09400") |
| md5 | Ran | deepmind/brave/brave/download_hmdb.py code served (permissive licence) · get_code("8bd26bff58d47d3a") |
| pcm_to_log_mel_spectrogram | Ran | deepmind/brave/brave/datasets/spectrograms.py code served (permissive licence) · get_code("f7f43698d451909c") |
| random_sample | Ran | deepmind/brave/brave/datasets/sampling.py code served (permissive licence) · get_code("e6ad315cfb7d7612") |
| tf_record_shard_reader | Ran | deepmind/brave/brave/datasets/media_sequences.py code served (permissive licence) · get_code("ca07ab87c895635c") |
| write_tf_record_dataset_fixture | Ran | deepmind/brave/brave/datasets/fixtures.py code served (permissive licence) · get_code("4c090fcd18780345") |
| media_sequence_dataset | Not yet run | deepmind/brave/brave/datasets/media_sequences.py code served (permissive licence) · get_code("315e821fb79e66f7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by image techniques, where these two views are similarly extracted by cropping and augmenting the resulting crop. However, these methods miss a crucial element in the video domain: time. We introduce BraVe, a self-supervised learning framework for video. In BraVe, one of the views has access to a narrow temporal window of the video while the other view has a broad access to the video content. Our models learn to generalise from the narrow view to the general content of the video. Furthermore, BraVe processes the views with different backbones, enabling the use of alternative augmentations or modalities into the broad view such as optical flow, randomly convolved RGB frames, audio or their combinations. We demonstrate that BraVe achieves stateof-the-art results in self-supervised representation learning on standard video and audio classification benchmarks including UCF101, HMDB51, Kinetics, ESC-50 and AudioSet.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2103.16559")
get_code_for_paper("2103.16559")
have("2103.16559")
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