most citedLUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20241 cited

PVUW 2024 Challenge on Complex Video Understanding: Methods and Results

Henghui Ding, Chang Liu, Yunchao Wei +34

Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentat…

cs.CV2024

RaFE: Generative Radiance Fields Restoration

Zhongkai Wu, Ziyu Wan, Jing Zhang +2

NeRF (Neural Radiance Fields) has demonstrated tremendous potential in novel view synthesis and 3D reconstruction, but its performance is sensitive to input image quality, which st…

cs.CV20244 cited

LUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images

Jing Zhang, Irving Fang, Juexiao Zhang +7

Lithic Use-Wear Analysis (LUWA) using microscopic images is an underexplored vision-for-science research area. It seeks to distinguish the worked material, which is critical for un…

cs.CV20231 cited

Multi-dimension Queried and Interacting Network for Stereo Image Deraining

Yuanbo Wen, Tao Gao, Ziqi Li +2

Eliminating the rain degradation in stereo images poses a formidable challenge, which necessitates the efficient exploitation of mutual information present between the dual views.…

cs.CV2023

Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation

Mochu Xiang, Jing Zhang, Nick Barnes +1

Effectively measuring and modeling the reliability of a trained model is essential to the real-world deployment of monocular depth estimation (MDE) models. However, the intrinsic i…