Generating a description of an image is called image captioning. Image captioning requires
recognizing the important objects, their attributes, and their relationships in an image. It also …

Generating a natural language description from an image is an emerging interdisciplinary
problem at the intersection of computer vision, natural language processing, and artificial …

We introduce GQA, a new dataset for real-world visual reasoning and compositional
question answering, seeking to address key shortcomings of previous VQA datasets. We …

We propose a novel attention gate (AG) model for medical image analysis that automatically
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …

We propose a novel attention gate (AG) model for medical imaging that automatically learns
to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly …

Neural network-based methods for abstractive summarization produce outputs that are more
fluent than other techniques, but which can be poor at content selection. This work proposes …

We introduce a general-purpose conditioning method for neural networks called FiLM:
Feature-wise Linear Modulation. FiLM layers influence neural network computation via a …

Text-image cross-modal retrieval is a challenging task in the field of language and vision.
Most previous approaches independently embed images and sentences into a joint …

Abstract We introduce the Neural State Machine, seeking to bridge the gap between the
neural and symbolic views of AI and integrate their complementary strengths for the task of …

Training a referring expression comprehension (ReC) model for a new visual domain
requires collecting referring expressions, and potentially corresponding bounding boxes, for …