Abstract
Universal Domain Adaptation (UniDA) focuses on transferring source domain knowledge to the target domain under both domain shift and unknown category shift. Its main challenge lies in identifying common class samples and aligning them. Current methods typically obtain target domain semantics centers from an unconstrained continuous image representation space. Due to domain shift and the unknown number of clusters, these centers often result in complex and less robust alignment algorithm. In this paper, based on vision-language models, we search for semantic centers in a semantically meaningful and discrete text representation space. The constrained space ensures almost no domain bias and appropriate semantic granularity for these centers, enabling a simple and robust adaptation algorithm. Specifically, we propose TArget Semantics Clustering (TASC) via Text Representations, which leverages information maximization as a unified objective and involves two stages. First, with the frozen encoders, a greedy search-based framework is used to search for an optimal set of text embeddings to represent target semantics. Second, with the search results fixed, encoders are refined based on gradient descent, simultaneously achieving robust domain alignment and private class clustering. Additionally, we propose Universal Maximum Similarity (UniMS), a scoring function tailored for detecting open-set samples in UniDA. Experimentally, we evaluate the universality of UniDA algorithms under four category shift scenarios. Extensive experiments on four benchmarks demonstrate the effectiveness and robustness of our method, which has achieved state-of-the-art performance.
Abstract (translated)
通用领域适应（UniDA）专注于在域偏移和未知类别迁移的情况下，将源领域的知识转移到目标领域。其主要挑战在于识别共同类别的样本并进行对齐。目前的方法通常从一个不受限制的连续图像表示空间中获取目标域语义中心，由于域偏移以及簇的数量未知，这些中心往往导致复杂的、鲁棒性较差的对齐算法。 在本文中，基于视觉-语言模型，我们在一个具有意义且离散的文本表示空间中搜索语义中心。这种受约束的空间确保了几乎没有领域偏差，并为这些中心提供了适当的语义粒度，从而能够实现简单且稳健的适应算法。具体来说，我们提出了通过文本表示进行目标语义聚类（TASC），该方法利用信息最大化作为统一的目标，并涉及两个阶段。首先，在冻结编码器的情况下，使用基于贪心搜索的框架来寻找最优的一组文本嵌入以表征目标领域的语义。其次，在固定了搜索结果之后，根据梯度下降对编码器进行细化，同时实现稳健的域对齐和私有类别聚类。 此外，我们提出了为检测UniDA中的开放集样本量身定制的评分函数——通用最大相似性（UniMS）。 在实验中，我们在四种类别迁移场景下评估了UniDA算法的普适性。广泛的基准测试表明，我们的方法具有有效性和鲁棒性，并取得了最先进的性能表现。
URL
https://arxiv.org/abs/2506.03521
