67 citations · 221 across the 42 of their papers we have counts for
14 papers · 1 filter
T-MLA: A targeted multiscale log-exponential attack framework for neural image compression
Nikolay I. Kalmykov, Razan Dibo, Kaiyu Shen +4
Neural image compression (NIC) has become the state-of-the-art for rate-distortion performance, yet its security vulnerabilities remain significantly less understood than those of…
From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training
Yipeng Liu, Qi Yang, Yujie Zhang +3
We present a novel quality assessment method which can predict the perceptual quality of point clouds from new scenes without available annotations by leveraging the rich prior kno…
Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA
Lanlan Feng, Ce Zhu, Yipeng Liu +2
Robust tensor principal component analysis (RTPCA) aims to separate the low-rank and sparse components from multi-dimensional data, making it an essential technique in the signal p…
Learning Disentangled Representations for Perceptual Point Cloud Quality Assessment via Mutual Information Minimization
Ziyu Shan, Yujie Zhang, Yipeng Liu +1
No-Reference Point Cloud Quality Assessment (NR-PCQA) aims to objectively assess the human perceptual quality of point clouds without relying on pristine-quality point clouds for r…
FunOTTA: On-the-Fly Adaptation on Cross-Domain Fundus Image via Stable Test-time Training
Qian Zeng, Le Zhang, Yipeng Liu +2
Fundus images are essential for the early screening and detection of eye diseases. While deep learning models using fundus images have significantly advanced the diagnosis of multi…
DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection
Ruituo Wu, Yang Chen, Jian Xiao +5
Cooperation between temporal convolutional networks (TCN) and graph convolutional networks (GCN) as a processing module has shown promising results in skeleton-based video anomaly…
Multi-view MERA Subspace Clustering
Zhen Long, Ce Zhu, Jie Chen +3
Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in the self-representation tensor. Current tensor decompositions for MSC suffer from highly unb…
Illumination-insensitive Binary Descriptor for Visual Measurement Based on Local Inter-patch Invariance
Xinyu Lin, Yingjie Zhou, Xun Zhang +2
Binary feature descriptors have been widely used in various visual measurement tasks, particularly those with limited computing resources and storage capacities. Existing binary de…
Level-line Guided Edge Drawing for Robust Line Segment Detection
Xinyu Lin, Yingjie Zhou, Yipeng Liu +1
Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract…
Adaptively Topological Tensor Network for Multi-view Subspace Clustering
Yipeng Liu, Yingcong Lu, Weiting Ou +2
Multi-view subspace clustering methods have employed learned self-representation tensors from different tensor decompositions to exploit low rank information. However, the data str…
A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges
Xinyu Lin, Yingjie Zhou, Yipeng Liu +1
An image line segment is a fundamental low-level visual feature that delineates straight, slender, and uninterrupted portions of objects and scenarios within images. Detection and…
No-Reference Point Cloud Quality Assessment via Domain Adaptation
Qi Yang, Yipeng Liu, Siheng Chen +2
We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds. For quality assessment, deep neural…
Scalable Deep Compressive Sensing
Zhonghao Zhang, Yipeng Liu, Xingyu Cao +2
Deep learning has been used to image compressive sensing (CS) for enhanced reconstruction performance. However, most existing deep learning methods train different models for diffe…
Decision-based Universal Adversarial Attack
Jing Wu, Mingyi Zhou, Shuaicheng Liu +2
A single perturbation can pose the most natural images to be misclassified by classifiers. In black-box setting, current universal adversarial attack methods utilize substitute mod…