Domain-generalized token linking in vision foundation models for semantic segmentation

• This work proposes a novel domain-generalized token linking strategy. • A text-guided dual token linking is proposed to learn domain-invariant tokens. • The text-guided distribution normalization aligns distributions of tokens and texts. • The proposed approach gains superior performance on multiple DGSS benchmarks. [S U M M A R Y] Vision Foundation Models (VFMs) achieve remarkable performance compared with traditional methods based on convolutional neural networks and vision transformer networks in Domain-Generalized Semantic Segmentation (DGSS). These VFM-based DGSS methods focus on adopting efficient parameter fine-tuning strategies that use a set of learnable tokens to fine-tune VFMs to the downstream DGSS task, yet struggle to mine domain-invariant information from VFMs since the backbone of VFMs is frozen during the fine-tuning stage. To address this issue, a Domain-Generalized Token Linking (DGTL) approach is proposed to mine domain-invariant information from VFMs for improving the performance in unseen target domains, which contains a Text-guided Dual Token Linking (TDTL) module and a Text-guided Distribution Normalization (TDN) strategy. For the TDTL module, first, a set of learnable tokens is linked to the text embeddings for building the relations between the learnable tokens and text embeddings, which is beneficial for learning domain-invariant tokens since the text embeddings generated from the CLIP model are domain-invariant. Second, the feature-level and mask-level linking strategies are proposed to link the learned domain-invariant tokens to the features and masks to guide the mining of domain-invariant information from the VFM. For the TDN strategy, the pairwise similarity between the predictive masks associated with the learnable tokens and the text embeddings is utilized to explicitly align the semantic distribution of visual features in the learnable tokens with the text embeddings. Extensive experiments demonstrate that the DGTL approach achieves superior performance to recent methods across multiple DGSS benchmarks. The code is released on GitHub: https://github.com/seabearlmx/DGTL .