We propose a method that can generate an unambiguous description (known as a referring
expression) of a specific object or region in an image, and which can also comprehend or …

Although existing image caption models can produce promising results using recurrent
neural networks (RNNs), it is difficult to guarantee that an object we care about is contained …

Current image captioning systems perform at a merely descriptive level, essentially
enumerating the objects in the scene and their relations. Humans, on the contrary, interpret …

Recently, the state-of-the-art models for image captioning have overtaken human
performance based on the most popular metrics, such as BLEU, METEOR, ROUGE and …

While recent deep neural network models have achieved promising results on the image
captioning task, they rely largely on the availability of corpora with paired image and …

Image captioning has received significant attention with remarkable improvements in recent
advances. Nevertheless, images in the wild encapsulate rich knowledge and cannot be …

Significant progress has been made on visual captioning, largely relying on pre-trained
features and later fixed object detectors that serve as rich inputs to auto-regressive models …

Automatically describing the content of an image is a fundamental problem in artificial
intelligence that connects computer vision and natural language processing. In this paper …

Deep neural networks have achieved great successes on the image captioning task.
However, most of the existing models depend heavily on paired image-sentence datasets …

We present an image caption system that addresses new challenges of automatically
describing images in the wild. The challenges include generating high quality caption with …