19 accepted papers
The Tensor Nuclear Norm (TNN), derived from the tensor singular value decomposition, is a widely used low-rank modeling tool that enforces element-wise sparsity on frequency-domain singular values. However, as a direct extension of the matrix nuclear norm, TNN fundamentally assumes single-level spec…
Adversarial purification uses generative models to restore clean data distributions from unseen attacks without retraining classifiers. However, unimodal diffusion-based approaches struggle to preserve semantic consistency, while recent multimodal variants rely on computationally expensive adversari…
Despite recent advances in image-to-video generation, better controllability and local animation are less explored. Most existing image-to-video methods are not locally aware and tend to move the entire scene. However, human artists may need to control the movement of different objects or regions. A…
Tensor regression is a powerful tool for analyzing complex multi-dimensional data in fields such as neuroimaging and spatiotemporal analysis, but its effectiveness is often hindered by insufficient sample sizes. To overcome this limitation, we adopt a transfer learning strategy that leverages knowle…
Recent text-to-image (T2I) diffusion models have demonstrated remarkable capabilities in visual synthesis, yet their performance heavily relies on the quality of input prompts. However, optimizing discrete prompts remains challenging because the discrete nature of tokens prevents the direct applicat…
Controllable character image animation has a wide range of applications. Although existing studies have consistently improved performance, challenges persist in the field of character image animation, particularly concerning stability in complex backgrounds and tasks involving multiple characters. T…
Despite the growing success of transform-based tensor models such as the t-product, their underlying geometric principles remain poorly understood. Classical differential geometry, built on real-valued function spaces, is not well suited to capture the algebraic and spectral structure induced by tra…
Deep Multi-view Clustering (DMVC) stands out as a widely adopted technique aiming at enhanced clustering performance by leveraging diverse data sources. However, the critical issue of vulnerability to adversarial attacks is unexplored due to the lack of well-defined attack objectives. To fill this c…
In adversarial defense, adversarial purification can be viewed as a special generation task with the purpose to remove adversarial attacks and diffusion models excel in adversarial purification for their strong generative power. With different predetermined generation requirements, various types of…
In multi-output regression, we identify a previously neglected challenge that arises from the inability of training distribution to cover all combinations of input features, leading to combinatorial distribution shift (CDS). To the best of our knowledge, this is the first work to formally define and…
Reasoning from visual dynamics scenes has many real world applications. However existing video reasoning benchmarks are still inadequate since they were mainly designed for factual or situated reasoning and rarely involve broader knowledge in the real world. Our work aims to delve deeper into reason…
Conventional Outlier Robust Tensor Decomposition (ORTD) approaches generally represent sparse outlier corruption within a specific mode. However, such an assumption, which may hold for matrices, proves inadequate when applied to high-order tensors. In the tensor domain, the outliers are prone to be…
Multi-channel learning has gained significant attention in recent applications, where neural networks with t-product layers (t-NNs) have shown promising performance through novel feature mapping in the transformed domain. However, despite the practical success of t-NNs, the theoretical analysis of…
To interpret deep networks, one main approach is to associate neurons with human-understandable concepts. However, existing methods often ignore the inherent connections of different concepts (e.g., dog and cat both belong to animals), and thus lose the chance to explain neurons responsible for high…
"Exemplar-based colorization approaches rely on reference image to provide plausible colors for target gray-scale image. The key and difficulty of exemplar-based colorization is to establish an accurate correspondence between these two images. Previous approaches have attempted to construct such a c…
Epileptic focus localization in patients with epileptic seizures is essential when surgery is needed. Recent studies show that this can be done automatically using machine learning approaches. However, well-designed feature extraction methods are often computationally demanding, requiring a large am…
Due to the superiority in exploiting the ubiquitous "spatial-shifting" property in modern multi-way data, the recently proposed low-tubal-rank model has been successfully applied for tensor recovery in signal processing and computer vision. In this paper, we define the generalized tensor Dantzig sel…
Tensor completion arouses much attention in signal processing and machine learning. The tensor train (TT) decomposition has shown better performances than the Tucker decomposition in image and video inpainting. In this paper, we propose a novel tensor completion model based on a newly defined latent…
Real multi-way data may suffer from missing entries, noise and outliers simultaneously. The recently proposed tubal nuclear norm (TNN) has shown its superiority in tensor completion. However, statistical analysis of TNN based models is still deficient. This paper aims to robustly recover a polluted…