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Boosting Text-Driven Video Segmentation via Geometry-Aware Distillation
Tianyu Zhu, Yingping Liang, Hesong Li +1
Text-driven Referring Video Object Segmentation (RVOS) aims to locate and segment target objects in videos given natural language. However, existing models are typically trained on…
Learning Dense Feature Matching via Lifting Single 2D Image to 3D Space
Yingping Liang, Yutao Hu, Wenqi Shao +1
Feature matching plays a fundamental role in many computer vision tasks, yet existing methods heavily rely on scarce and clean multi-view image collections, which constrains their…
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather
Yuran Wang, Yingping Liang, Yutao Hu +1
Learning-based stereo matching models struggle in adverse weather conditions due to the scarcity of corresponding training data and the challenges in extracting discriminative feat…
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images
Yingping Liang, Ying Fu, Yutao Hu +3
Optical flow estimation is a crucial subfield of computer vision, serving as a foundation for video tasks. However, the real-world robustness is limited by animated synthetic datas…
Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World
Yuran Wang, Yingping Liang, Ying Fu
Stereo matching methods rely on dense pixel-wise ground truth labels, which are laborious to obtain, especially for real-world datasets. The scarcity of labeled data and domain gap…
Distilling Monocular Foundation Model for Fine-grained Depth Completion
Yingping Liang, Yutao Hu, Wenqi Shao +1
Depth completion involves predicting dense depth maps from sparse LiDAR inputs. However, sparse depth annotations from sensors limit the availability of dense supervision, which is…