Hao Li, Chang Liu, Jie Chen, Li Yuan, Jin Peng, Jinfa Huang, Zesen Cheng, Zhennan Wang
We lifted 23 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 |
|---|---|---|
| jpthu17/DiCoSA | canonical | 7 of 16 |
| jpthu17/diffusionret | canonical | 1 of 4 |
| jpthu17/HBI | — | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| swish | Ran | jpthu17/DiCoSA/tvr/models/until_module.py code served (permissive licence) · get_code("0f786c407fb1ee4c") |
| basic_clean | Ran | jpthu17/DiCoSA/tvr/models/tokenization_clip.py code served (permissive licence) · get_code("98f385d847636a3e") |
| euclidean_dist | Ran | jpthu17/DiCoSA/tvr/models/until_module.py code served (permissive licence) · get_code("e54804982d1e6d22") |
| gelu | Ran | jpthu17/DiCoSA/tvr/models/until_module.py code served (permissive licence) · get_code("50e1ffed03f484ec") |
| get_pairs | Ran | jpthu17/DiCoSA/tvr/models/tokenization_clip.py code served (permissive licence) · get_code("d919ae32e5e4e616") |
| merge_tokens | Ran | jpthu17/HBI/HBI/models/cluster.py code served (permissive licence) · get_code("0e49170b7c8f60fd") |
| url_to_filename | Ran | jpthu17/diffusionret/DiffusionRet/models/file_utils.py code served (permissive licence) · get_code("af64ec220e8bcdbc") |
| warmup_linear | Ran | jpthu17/DiCoSA/tvr/models/optimization.py code served (permissive licence) · get_code("e0960afdb0d64aa7") |
| whitespace_clean | Ran | jpthu17/DiCoSA/tvr/models/tokenization_clip.py code served (permissive licence) · get_code("9542161e9640b858") |
| CTM | Not yet run | jpthu17/HBI/HBI/models/cluster.py code served (permissive licence) · get_code("8bc3e8d24dc52e8b") |
| TokenConv | Not yet run | jpthu17/HBI/HBI/models/cluster.py code served (permissive licence) · get_code("8b50e3e4f063bce8") |
| cached_path | Not yet run | jpthu17/diffusionret/DiffusionRet/models/file_utils.py code served (permissive licence) · get_code("349d780dd4a89a37") |
| dataloader_activity_train | Not yet run | jpthu17/diffusionret/DiffusionRet/dataloaders/data_dataloaders.py code served (permissive licence) · get_code("ab028ff1220c10c1") |
| dataloader_msrvtt_test | Not yet run | jpthu17/DiCoSA/tvr/dataloaders/data_dataloaders.py code served (permissive licence) · get_code("b6070984a76e9bf3") |
| dataloader_msrvtt_train | Not yet run | jpthu17/DiCoSA/tvr/dataloaders/data_dataloaders.py code served (permissive licence) · get_code("780f499e676f8267") |
| dataloader_msrvtt_train_test | Not yet run | jpthu17/DiCoSA/tvr/dataloaders/data_dataloaders.py code served (permissive licence) · get_code("70d56cc28b0d7a14") |
| filename_to_url | Not yet run | jpthu17/diffusionret/DiffusionRet/models/file_utils.py code served (permissive licence) · get_code("db0ac56aaf6e35e6") |
| get_args | Not yet run | jpthu17/DiCoSA/main_retrieval.py code served (permissive licence) · get_code("91e7fe98724c5bad") |
| load_stopwords | Not yet run | jpthu17/DiCoSA/tvr/dataloaders/dataloader_activitynet_retrieval.py code served (permissive licence) · get_code("d227e7f9a9ea755e") |
| remove_stopwords | Not yet run | jpthu17/DiCoSA/tvr/dataloaders/dataloader_activitynet_retrieval.py code served (permissive licence) · get_code("1d11677b554a969d") |
| set_seed_logger | Not yet run | jpthu17/DiCoSA/main_retrieval.py code served (permissive licence) · get_code("8952edef143f2747") |
| warmup_constant | Not yet run | jpthu17/DiCoSA/tvr/models/optimization.py code served (permissive licence) · get_code("59e4e730a9dc5dc6") |
| warmup_cosine | Not yet run | jpthu17/DiCoSA/tvr/models/optimization.py code served (permissive licence) · get_code("20b3d25ba1b796e5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Text-video retrieval is a challenging cross-modal task, which aims to align visual entities with natural language descriptions. Current methods either fail to leverage the local details or are computationally expensive. What's worse, they fail to leverage the heterogeneous concepts in data. In this paper, we propose the Disentangled Conceptualization and Set-to-set Alignment (DiCoSA) to simulate the conceptualizing and reasoning process of human beings. For disentangled conceptualization, we divide the coarse feature into multiple latent factors related to semantic concepts. For set-to-set alignment, where a set of visual concepts correspond to a set of textual concepts, we propose an adaptive pooling method to aggregate semantic concepts to address partial matching. In particular, since we encode concepts independently in only a few dimensions, DiCoSA is superior at efficiency and granularity, ensuring fine-grained interactions using a similar computational complexity as coarse-grained alignment. Extensive experiments on five datasets, including MSR-VTT, LSMDC, MSVD, ActivityNet, and DiDeMo, demonstrate that our method outperforms the existing state-of-the-art methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2305.12218")
get_code_for_paper("2305.12218")
have("2305.12218")
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