6 accepted papers
Multimodal learning aims to capture both shared and private information from multiple modalities. However, existing methods that project all modalities into a single latent space for fusion often overlook the asynchronous, multi-level semantic structure of multimodal data. This oversight induces sem
Centralized multimodal learning commonly compresses language, acoustic, and visual signals into a single fused representation for prediction. While effective, this paradigm suffers from two limitations: modality dominance, where optimization gravitates towards the path of least resistance, ignoring …
Multimodal Sentiment Analysis integrates Linguistic, Visual, and Acoustic. Mainstream approaches based on modality-invariant and modality-specific factorization or on complex fusion still rely on spatiotemporal mixed modeling. This ignores spatiotemporal heterogeneity, leading to spatiotemporal info…
Multimodal Sentiment Analysis (MSA) integrates language, visual, and acoustic modalities to infer human sentiment. Most existing methods either focus on globally shared representations or modality-specific features, while overlooking signals that are shared only by certain modality pairs. This limit
In recent years, magnetically controlled microrobots have garnered significant attention. This paper presents the H-robot, a self-designed microrobot featuring an innovative structure. The H-robot features a honeycomb porous spherical design specifically engineered to enhance cargo capacity. A new d
In recent years, researchers have increasingly turned to marine organisms for inspiration in designing underwater robots. While most robots rely on jet propulsion, akin to squid or jellyfish, using a single posterior aperture for water intake and expulsion, there are few incorporating an additional