Progressive Hybrid Pseudo-Labeling for Unsupervised Domain Adaptation With Ascending Low-Rank Adaptation
Unsupervised domain adaptation (UDA) based on large vision-language models (VLMs) has recently demonstrated strong generalization ability, yet it remains fundamentally challenged by noisy pseudo-labels and inefficient adaptation under large domain shifts. In this paper, we propose Progressive Hybrid Pseudo-Labeling for UDA with Ascending Low-Rank Adaptation (termed as PHPL), a parameter-efficient paradigm that addresses these challenges from two complementary perspectives. 1) We introduce a progressive hybrid pseudo-labeling strategy that constructs target-domain supervision by fusing predictions from a frozen teacher model and an adaptive student model with a progressive weighting scheme. By gradually transferring predictive responsibility from the teacher to the student during training, PHPL effectively mitigates early-stage pseudo-label noise and stabilizes self-training under large domain shifts. 2) To enable efficient and stable adaptation of large VLMs, we propose an ascending low-rank adaptation strategy that allocates LoRA capacity in a depth-aware manner. Specifically, larger low-rank updates are assigned to deeper, semantically richer layers, while shallow layers remain lightly parameterized, striking a favorable balance between parameter efficiency and representational expressiveness. We conduct extensive experiments on five widely-used UDA benchmarks, including Office-Home, Office-31, VisDA-2017, Mini-DomainNet, and DomainNet. Experimental results verify that PHPL consistently achieves higher performance across various cross-domain scenarios compared with existing CNN, Transformer, and VLMs-based solutions. Notably, PHPL demonstrates strong robustness on highly challenging large-scale conditions while requiring significantly less computational overhead, validating the effectiveness and scalability of the proposed lightweight adaptation paradigm. The code is available at https://github.com/el2k/PHPL