Rui Zhu, Ziqi Wang, Xiaochun Cao, Wenqi Ren, Siyuan Yao, Yanyang Yan
We lifted 2 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| Z-Z188/UMDATrack | — | 1 of 1 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| UMDATrack | Ran | Z-Z188/UMDATrack/lib/models/UMDATrack/UMDATrack.py code served (permissive licence) · get_code("26e86bead3098467") |
| box_xyxy_to_cxcywh | Ran | this paper's copy was not recorded; identical code first harvested from edward-sun/tsp-detection pointer only · get_code("031b02fb990284ba") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Visual object tracking has gained promising progress in past decades. Most of the existing approaches focus on learning target representation in well-conditioned daytime data, while for the unconstrained real-world scenarios with adverse weather conditions, e.g. nighttime or foggy environment, the tremendous domain shift leads to significant performance degradation. In this paper, we propose UMDATrack, which is capable of maintaining high-quality target state prediction under various adverse weather conditions within a unified domain adaptation framework. Specifically, we first use a controllable scenario generator to synthesize a small amount of unlabeled videos (less than 2% frames in source daytime datasets) in multiple weather conditions under the guidance of different text prompts. Afterwards, we design a simple yet effective domain-customized adapter (DCA), allowing the target objects' representation to rapidly adapt to various weather conditions without redundant model updating. Furthermore, to enhance the localization consistency between source and target domains, we propose a target-aware confidence alignment module (TCA) following optimal transport theorem. Extensive experiments demonstrate that UMDATrack can surpass existing advanced visual trackers and lead new stateof-the-art performance by a significant margin. Our code is available at https://github.com/Z-Z188/UMDATrack.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2507.00648")
get_code_for_paper("2507.00648")
have("2507.00648")
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