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CLIP the Divergence: Language-guided Unsupervised Domain Adaptation
Jinjing Zhu, Yucheng Chen, Lin Wang
Unsupervised domain adaption (UDA) has emerged as a popular solution to tackle the divergence between the labeled source and unlabeled target domains. Recently, some research effor…
eMoE-Tracker: Environmental MoE-based Transformer for Robust Event-guided Object Tracking
Yucheng Chen, Lin Wang
The unique complementarity of frame-based and event cameras for high frame rate object tracking has recently inspired some research attempts to develop multi-modal fusion approache…
PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Mobius Spatial Augmentation
Zidong Cao, Jinjing Zhu, Weiming Zhang +4
Recently, Depth Anything Models (DAMs) - a type of depth foundation models - have demonstrated impressive zero-shot capabilities across diverse perspective images. Despite its succ…
Energy-based Domain-Adaptive Segmentation with Depth Guidance
Jinjing Zhu, Zhedong Hu, Tae-Kyun Kim +1
Recent endeavors have been made to leverage self-supervised depth estimation as guidance in unsupervised domain adaptation (UDA) for semantic segmentation. Prior arts, however, ove…
Source-Free Cross-Modal Knowledge Transfer by Unleashing the Potential of Task-Irrelevant Data
Jinjing Zhu, Yucheng Chen, Lin Wang
Source-free cross-modal knowledge transfer is a crucial yet challenging task, which aims to transfer knowledge from one source modality (e.g., RGB) to the target modality (e.g., de…