17 citations · 29 across the 15 of their papers we have counts for
5 papers · 1 filter
UIR-LoRA: Achieving Universal Image Restoration through Multiple Low-Rank Adaptation
Cheng Zhang, Dong Gong, Jiumei He +3
Existing unified methods typically treat multi-degradation image restoration as a multi-task learning problem. Despite performing effectively compared to single degradation restora…
Multi-Granularity Language-Guided Training for Multi-Object Tracking
Yuhao Li, Jiale Cao, Muzammal Naseer +4
Most existing multi-object tracking methods typically learn visual tracking features via maximizing dis-similarities of different instances and minimizing similarities of the same…
NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results
Xiaoning Liu, Zongwei Wu, Ao Li +109
This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective netw…
GoMVS: Geometrically Consistent Cost Aggregation for Multi-View Stereo
Jiang Wu, Rui Li, Haofei Xu +4
Matching cost aggregation plays a fundamental role in learning-based multi-view stereo networks. However, directly aggregating adjacent costs can lead to suboptimal results due to…
Boosting Multi-view Stereo with Late Cost Aggregation
Jiang Wu, Rui Li, Yu Zhu +3
Pairwise matching cost aggregation is a crucial step for modern learning-based Multi-view Stereo (MVS). Prior works adopt an early aggregation scheme, which adds up pairwise costs…