Title: Cross Modal Domain Generalization for Query-based Video Segmentation. Abstract:   Domain generalization (DG) aims to increase a model's generalization ability against the performance degradation when transferring to the target domains, which has been successfully applied in various visual and natural language tasks. However, DG on multi-modal tasks is still an untouched field. Compared with traditional single-modal DG, the biggest challenge of multi-modal DG is that each modality has to cope with its own domain shift. Directly applying the previous methods will make the generalization direction of the model in each modality inconsistent, resulting in negative effects when the model is migrated to the target domains. Thus in this paper, we explore the scenario of query-based video segmentation to study how to better advance the generalization ability of the model in the multi-modal situation. Considering the information from different modalities often shows consistency, we propose query-guided feature augmentation (QFA) and attention map adaptive instance normalization (AM-AdaIN) modules. Compared with traditional DG models, our method can combine visual and textual modalities together to guide each other for data augmentation and learn a domain-agnostic cross-modal relationship, which is more suitable for multi-modal transfer tasks. Extensive experiments on three query-based video segmentation generalization tasks demonstrate the effectiveness of our method. 