Challenges and Prospects in Vision and Language Research

Advancements in deep learning and the availability of large-scale datasets have resulted in great progress in computer vision and natural language processing (NLP). Deep convolutional neural networks (CNNs) have enabled unprecedented improvements in classical computer vision tasks, e.g., image classification and object detection. Progress in many NLP tasks has been similarly swift. Building upon these advances, there is a push to attack new problems that enable concept comprehension and reasoning capabilities to be studied at the intersection of vision and language (V&L) understanding. There are numerous applications for V&L systems, including enabling the visually impaired to interact with visual content using language, human-computer interaction, and visual search.