Despite progress in perceptual tasks such as image classification, computers still perform
poorly on cognitive tasks such as image description and question answering. Cognition is …

We propose the task of free-form and open-ended Visual Question Answering (VQA). Given
an image and a natural language question about the image, the task is to provide an …

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 …

Abstract Visual Question Answering (VQA) in its ideal form lets us study reasoning in the
joint space of vision and language and serves as a proxy for the AI task of scene …

We investigate the problem of producing structured graph representations of visual scenes.
Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We …

One of the most challenging question types in VQA is when answering the question requires
outside knowledge not present in the image. In this work we study open-domain knowledge …

Visual relations, such as" person ride bike" and" bike next to car", offer a comprehensive
scene understanding of an image, and have already shown their great utility in connecting …

Humans are increasingly coming into contact with artificial intelligence (AI) and machine
learning (ML) systems. Human‐centered AI is a perspective on AI and ML that algorithms …

We present a novel unsupervised feature representation learning method, Visual
Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an …

Cross-media analysis and reasoning is an active research area in computer science, and a
promising direction for artificial intelligence. However, to the best of our knowledge, no …