Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when
missing-modality occurs either during training or testing in real-world situations; and 2) when …

The missing modality issue is critical but non-trivial to be solved by multi-modal models.
Current methods aiming to handle the missing modality problem in multi-modal tasks, either …

Multimodal data collected from the real world are often imperfect due to missing modalities.
Therefore multimodal models that are robust against modal-incomplete data are highly …

Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

In the realm of video object tracking auxiliary modalities such as depth thermal or event data
have emerged as valuable assets to complement the RGB trackers. In practice most existing …

Federated learning (FL), which provides a collaborative training scheme for distributed data
sources with privacy concerns, has become a burgeoning and attractive research area. Most …

Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …

Abstract We propose an Explicit Conditional Multimodal Variational Auto-Encoder
(ECMVAE) for audio-visual segmentation (AVS), aiming to segment sound sources in the …