The focus of this survey is on the analysis of two modalities of multimodal deep learning:
image and text. Unlike classic reviews of deep learning where monomodal image classifiers …

We consider the problem of Visual Question Answering (VQA). Given an image and a free-
form, open-ended, question, expressed in natural language, the goal of VQA system is to …

Abstract Visual Question Answering (VQA) is a multi-disciplinary research problem that has
captured the attention of both computer vision as well as natural language processing …

Abstract The Visual Question Answering (VQA) task combines challenges for processing
data with both Visual and Linguistic processing, to answer basic 'common sense'questions …

Emotion Recognition in Conversations (ERC) is essential for building empathetic human-
machine systems. Existing studies on ERC primarily focus on summarizing the context …

This work evaluates GPT-4V's multimodal capability for medical image analysis, focusing on
three representative tasks radiology report generation, medical visual question answering …

Knowledge Graph Completion (KGC) aims at inferring missing entities or relations by
embedding them in a low-dimensional space. However, most existing KGC methods …

Abstract Medical Visual Question Answering (VQA) systems play a supporting role to
understand clinic-relevant information carried by medical images. The questions to a …

Answering semantically complicated questions according to an image is challenging in a
visual question answering (VQA) task. Although the image can be well represented by deep …

View change brings a significant challenge to action representation and recognition due to
pose occlusion and deformation. We propose a Global-Local Cross-View Fisher …