279 citations · 377 across the 7 of their papers we have counts for
7 papers
Prompt Tuning with Soft Context Sharing for Vision-Language Models
Kun Ding, Ying Wang, Pengzhang Liu +4
Vision-language models have recently shown great potential on many tasks in computer vision. Meanwhile, prior work demonstrates prompt tuning designed for vision-language models co…
HAN: An Efficient Hierarchical Self-Attention Network for Skeleton-Based Gesture Recognition
Jianbo Liu, Ying Wang, Shiming Xiang +1
Previous methods for skeleton-based gesture recognition mostly arrange the skeleton sequence into a pseudo picture or spatial-temporal graph and apply deep Convolutional Neural Net…
Accurate Urban Road Centerline Extraction from VHR Imagery via Multiscale Segmentation and Tensor Voting
Guangliang Cheng, Feiyun Zhu, Shiming Xiang +1
It is very useful and increasingly popular to extract accurate road centerlines from very-high-resolution (VHR) re- mote sensing imagery for various applications, such as road map…
10,000+ Times Accelerated Robust Subset Selection (ARSS)
Feiyun Zhu, Bin Fan, Xinliang Zhu +3
Subset selection from massive data with noised information is increasingly popular for various applications. This problem is still highly challenging as current methods are general…
Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity
Feiyun Zhu, Ying Wang, Bin Fan +2
Hyperspectral unmixing (HU) plays a fundamental role in a wide range of hyperspectral applications. It is still challenging due to the common presence of outlier channels and the l…
Spectral Unmixing via Data-guided Sparsity
Feiyun Zhu, Ying Wang, Bin Fan +3
Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral…
Robust Hyperspectral Unmixing with Correntropy based Metric
Ying Wang, Chunhong Pan, Shiming Xiang +1
Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The problem of hyperspectral unmixing has proven to be a difficult task in unsupervised work…