10 accepted papers
Recommendation systems seek to accurately model user preferences from a large set of candidate items. Graph neural networks (GNNs) have emerged as a dominant approach in this domain due to their ability to capture high-order user–item interactions. Recent efforts have aimed to enhance GNN-based repr…
Incomplete multi-view clustering (IMVC) aims to exploit complementary information from multiple views with missing observations. Recent diffusion-based approaches have shown promise for view completion; however, they often fail to capture instance-aligned global correlations across views and suffer
Explainability for graph neural networks (GNNs) aims to unveil the complex decision logic of learned models by identifying the most influential structures in the input graph, thereby improving transparency and trustworthiness. Existing post-hoc explainers typically extract a sparse key subgraph at a…
Alzheimer's Disease patients often exhibit cognitive decline, with language impairment being a prominent biomarker. Spontaneous speech analysis provides a non-invasive screening approach for AD. Large language models, increasingly employed for textual feature extraction, show potential in early AD p…
As an important research direction in the field of speech signal processing, cross-domain speech emotion recognition (SER) has attracted extensive attention. In practice, it is challenging to collect enough labeled samples from single source domain to train robust classifiers. To this end, this pape…
Visuotactile sensors have gained attention during the last years in robotics because they are able to reconstruct with high precision the 3D contact shape (or mesh) between the robotic fingers and the object. A new slip detection and avoidance algorithm is proposed based on the dynamic variation of
In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning…
Adversarial attack on point clouds plays a vital role in evaluating and improving the adversarial robustness of 3D deep learning models. Current attack methods are mainly applied by point perturbation in a non-manifold manner. In this paper, we formulate a novel manifold attack, which deforms the un…
Deformable object grasping has great potential to improve the versatility of robotic manipulation and extend the applications of robotics. It has thus attracted more and more attentions and efforts from robotic community. Nevertheless, the deformation of objects during grasping leads to difficulties
In practical situations, the emotional speech utterances are often collected from different devices and conditions, which will obviously affect the recognition performance. To address this issue, in this paper, a novel transfer non-negative matrix factorization (TNMF) method is presented for cross-c…