8 papers
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…
DeepNuParc: A Novel Deep Clustering Framework for Fine-scale Parcellation of Brain Nuclei Using Diffusion MRI Tractography
Haolin He, Ce Zhu, Le Zhang +9
Brain nuclei are clusters of anatomically distinct neurons that serve as important hubs for processing and relaying information in various neural circuits. Fine-scale parcellation…
Fast randomized Kronecker tensor decomposition: algorithms and error analysis
Salman Ahmadi-Asl, Naeim Rezaeian, Andre L. F. de Almeida +1
This paper proposes fast randomized algorithms for computing the Kronecker Tensor Decomposition (KTD) by replacing the sequence of deterministic SVDs in the TTr1SVD framework with…
Differentiable Low-computation Global Correlation Loss for Monotonicity Evaluation in Quality Assessment
Yipeng Liu, Qi Yang, Yiling Xu
In this paper, we propose a global monotonicity consistency training strategy for quality assessment, which includes a differentiable, low-computation monotonicity evaluation loss…
Once-Training-All-Fine: No-Reference Point Cloud Quality Assessment via Domain-relevance Degradation Description
Yipeng Liu, Qi Yang, Yujie Zhang +4
The visual quality of point clouds plays a crucial role in the development and broadcasting of immersive media. Therefore, investigating point cloud quality assessment (PCQA) is in…