6 papers
AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference
Hangfeng Liang, Yutao Hu, Yanhan Hu +3
Low-light video enhancement (LLVE) remains a challenging task due to severe information degradation under low-illumination conditions. Recent multimodal approaches have significant…
Car-1000: A New Large Scale Fine-Grained Visual Categorization Dataset
Yutao Hu, Sen Li, Jincheng Yan +2
Fine-grained visual categorization (FGVC) is a challenging but significant task in computer vision, which aims to recognize different sub-categories of birds, cars, airplanes, etc.…
StructDiff: Structure-aware Diffusion Model for 3D Fine-grained Medical Image Synthesis
Jiahao Xia, Yutao Hu, Yaolei Qi +6
Solving medical imaging data scarcity through semantic image generation has attracted growing attention in recent years. However, existing generative models mainly focus on synthes…
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…
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…
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…