5 accepted papers
Anomaly detection in dynamic graphs is essential for monitoring evolving systems such as transaction networks and online platforms. Yet existing methods remain limited in realistic edge-stream settings: snapshot-based approaches discretize continuous interactions and miss fine-grained temporal signa
We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generative modeling framework that learns velocity fields between probability distributions and has shown strong performance co…
Speech bandwidth extension (BWE) has demonstrated promising performance in enhancing the perceptual speech quality in real communication systems. Most existing BWE researches primarily focus on fixed upsampling ratios, disregarding the fact that the effective bandwidth of captured audio may fluctuat…
Multimodal Sentiment Analysis (MSA) aims to yield the sentiment polarities of speakers in video streams based on multiple modal features such as textual, acoustic and visual features, and has attracted amounts of attention in recent years. Existing MSA models often yield unimodal embeddings from the…
Rumors spread rapidly through online social microblogs at a relatively low cost, causing substantial economic losses and negative consequences in our daily lives. Existing rumor detection models often neglect the underlying semantic coherence between text and image components in multimodal posts, as…