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 …

Large-scale task planning is a major challenge. Recent work exploits large language
models (LLMs) directly as a policy and shows surprisingly interesting results. This paper …

Multi-view clustering can explore common semantics from multiple views and has attracted
increasing attention. However, existing works punish multiple objectives in the same feature …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Visual question answering (VQA) is designed to examine the visual-textual reasoning ability
of an intelligent agent. However, recent observations show that many VQA models may only …

We present a framework that formulates visual question answering as modular code
generation. In contrast to prior work on modular approaches to VQA, our approach requires …

With the increasing availability of multimodal educational data, there is a growing need to
effectively integrate and exploit multiple data sources to enhance student engagement …

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Visual question answering (VQA) is a prevalent task in real-world, and plays an essential
role in helping the blind understand the physical world. However, due to the real-world …