7 papers
Suppress and Diversify: Refining Robust Pathways for Corruption Robustness
Jiangang Yang, Wenhui Shi, Xiaoran Xu +4
Model robustness against natural image corruptions is essential for safety-critical applications. While existing methods primarily focus on implicit representation learning, we pro…
VTM-Nav: Harnessing Cross-Episode Experience for Object-Goal Navigation with Hierarchical Visual-Topological Memory
Xiaoran Xu, Yupeng Wu, Tianyu Xue +4
Training-free ObjectNav agents increasingly use vision-language models (VLMs), yet typically discard acquired scene knowledge after each request. We study cross-episode ObjectNav,…
Hy-Embodied-VLM-1.0: Efficient Physical-World Agents
Ziyi Wang, Xumin Yu, Yongming Rao +19
Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situatio…
DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation
Xiaoran Xu, Yifan Xu, Yupeng Wu +2
Test-time adaptation (TTA) aims to align a model to shifting test domains using only unlabeled streaming data. Most existing methods implicitly infer a single global domain distrib…
Towards Domain-Generalized Open-Vocabulary Object Detection: A Progressive Domain-invariant Cross-modal Alignment Method
Xiaoran Xu, Xiaoshan Yang, Jiangang Yang +3
Open-Vocabulary Object Detection (OVOD) has achieved remarkable success in generalizing to novel categories. However, this success often rests on the implicit assumption of domain…
Boosting Single-domain Generalized Object Detection via Vision-Language Knowledge Interaction
Xiaoran Xu, Jiangang Yang, Wenyue Chong +4
Single-Domain Generalized Object Detection~(S-DGOD) aims to train an object detector on a single source domain while generalizing well to diverse unseen target domains, making it s…