Rui Zhang, Xin Zhang, Yunji Chen, Ling Li, Haochen Li, Hantao Yao, Xinkai Song, Yifan Hao, Yongwei Zhao, Xiaqing Li
We lifted 6 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| Therock90421/DA-Ada | canonical | 4 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| convert_basic_c2_names | Ran | Therock90421/DA-Ada/detectron2/checkpoint/c2_model_loading.py code served (permissive licence) · get_code("e8526a516b4f9206") |
| convert_basic_clip_names | Ran | Therock90421/DA-Ada/detectron2/checkpoint/clip_model_loading.py code served (permissive licence) · get_code("7b7f6e4aa28b1a69") |
| convert_c2_detectron_names | Ran | Therock90421/DA-Ada/detectron2/checkpoint/c2_model_loading.py code served (permissive licence) · get_code("e36f87059208bb7e") |
| convert_clip_names | Ran | Therock90421/DA-Ada/detectron2/checkpoint/clip_model_loading.py code served (permissive licence) · get_code("376248f5d186a885") |
| align_and_update_state_dicts | Not yet run | Therock90421/DA-Ada/detectron2/checkpoint/c2_model_loading.py code served (permissive licence) · get_code("ac2638463572c152") |
| align_and_update_state_dicts_for_CLIP | Not yet run | Therock90421/DA-Ada/detectron2/checkpoint/clip_model_loading.py code served (permissive licence) · get_code("0bd7b0a60b4069f9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. As the visual-language models (VLMs) can provide essential general knowledge on unseen images, freezing the visual encoder and inserting a domain-agnostic adapter can learn domaininvariant knowledge for DAOD. However, the domain-agnostic adapter is inevitably biased to the source domain. It discards some beneficial knowledge discriminative on the unlabelled domain, i.e.domain-specific knowledge of the target domain. To solve the issue, we propose a novel Domain-Aware Adapter (DA-Ada) tailored for the DAOD task. The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariant knowledge. DA-Ada consists of the Domain-Invariant Adapter (DIA) for learning domain-invariant knowledge and the Domain-Specific Adapter (DSA) for injecting the domain-specific knowledge from the information discarded by the visual encoder. Comprehensive experiments over multiple DAOD tasks show that DA-Ada can efficiently infer a domain-aware visual encoder for boosting domain adaptive object detection. Our code is available at https://github.com/Therock90421/DA-Ada
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2410.09004")
get_code_for_paper("2410.09004")
have("2410.09004")
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