10 accepted papers
In real-world scenarios, new views are continuously collected over time, forming a dynamic view stream. To handle such evolving data, a lifelong multi-view clustering framework is needed instead of a static model. However, large discrepancies across views make it challenging to learn new knowledge w
Multimodal image fusion integrates complementary information from different modalities. However, large cross-modal discrepancies and local conflicts often introduce uncertainty into fusion decisions. This uncertainty can bias modality allocation in inconsistent regions, leading to information loss o…
In semi‑supervised multi‑view classification (SMVC), scarce labels and noisy unlabeled data impair feature aggregation and compromise prediction reliability, while existing methods lack principled guidance and interpretability. To overcome these limitations, we propose a novel unified SMVC framework
Infrared and visible image fusion (IVIF) technology has become a frontier of great interest due to the ability to integrate information from multiple sources. However, the progressive slowdown of weight updates in deep networks (i.e., “network laziness” phenomenon), makes existing methods far from r
Existing deep multi-view clustering methods have demonstrated excellent performance, which addressing issues such as missing views and view noise. But almost all existing methods are within a static framework, which assumes that all views have already been collected. However, in practical scenarios,…
In recent years, multi-view learning has aroused extensive research passion. Most existing multi-view learning methods often rely on well-annotations to improve decision accuracy. However, noise labels are ubiquitous in multi-view data due to imperfect annotations. To deal with this problem, we prop…
In recent years, multi-view clustering (MVC) has become a promising approach for analyzing heterogeneous multi-source data. However, during the collection of multi-view data, factors such as environmental interference or sensor failure often lead to the loss of view sample data, resulting in incompl
In recent years, anchor and hash-based multi-view clustering methods have gained attention for their efficiency and simplicity in handling large-scale data. However, existing methods often overlook the interactions among multi-view data and higher-order cooperative relationships during projection, n…
Anchor based pair-wised multi-view clustering often assumes multi-view data are paired, and has demonstrated significant advancements in recent years. However, this presumption is easily violated, and data is commonly unpaired fully in practical applications due to the influence of data collection a…
The prevalent approach in speech emotion recognition (SER) involves integrating both audio and textual information to comprehensively identify the speaker’s emotion, with the text generally obtained through automatic speech recognition (ASR). An essential issue of this approach is that ASR errors fr…