7 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…
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…
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…
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…