Papers › Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain...
Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation
Liang Chen, Yihang Lou, Jianzhong He, Tao Bai, Minghua Deng
Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label space. However, domain shift and category shift make UniDA extremely challenging, which mainly lies in how to recognize both shared "known" samples and private "unknown" samples. Previous works rarely explore the intrinsic geometrical relationship between the two domains, and they manually set a threshold for the overconfident closed-world classifier to reject "unknown" samples. Therefore, in this paper, we propose a Geometric anchor-guided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues. Specifically, we first develop a random walk-based anchor mining strategy together with a high-order attention mechanism to build correspondence across domains. Then a global joint local domain alignment paradigm is designed, i.e., geometric adversarial learning for global distribution calibration and subgraph-level contrastive learning for local region aggregation. Toward accurate target private samples detection, GATE introduces a universal incremental classifier by modeling the energy uncertainty. We further efficiently generate novel categories by manifold mixup, and minimize the open-set entropy to learn the "unknown" threshold adaptively. Extensive experiments on three benchmarks demonstrate that GATE significantly outperforms previous state-of-the-art UniDA methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Universal Domain Adaptation | Office-31 | GATE | H-score | 87.6 | #7 of 12 | Archive leaderboard | report |
| Universal Domain Adaptation | Office-31 | GATE | Source-Free | no | #7 of 12 | Archive leaderboard | report |
| Universal Domain Adaptation | Office-Home | GATE | H-Score | 75.6 | #7 of 14 | Archive leaderboard | report |
| Universal Domain Adaptation | Office-Home | GATE | Source-free | no | #7 of 14 | Archive leaderboard | report |
| Universal Domain Adaptation | Office-Home | GATE | VLM | no | #7 of 14 | Archive leaderboard | report |
| Universal Domain Adaptation | VisDA2017 | GATE | H-score | 56.4 | #9 of 13 | Archive leaderboard | report |
| Universal Domain Adaptation | VisDA2017 | GATE | Source-free | no | #9 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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