Modeling Context in Referring Expressions
This work addresses the challenge of object reference in dialogue for computer vision and natural language processing, with incremental improvements in model performance.
The paper tackled the problem of generating and comprehending natural language referring expressions for objects in images by incorporating visual context and joint generation methods, resulting in improved performance on three datasets (RefCOCO, RefCOCO+, and RefCOCOg).
Humans refer to objects in their environments all the time, especially in dialogue with other people. We explore generating and comprehending natural language referring expressions for objects in images. In particular, we focus on incorporating better measures of visual context into referring expression models and find that visual comparison to other objects within an image helps improve performance significantly. We also develop methods to tie the language generation process together, so that we generate expressions for all objects of a particular category jointly. Evaluation on three recent datasets - RefCOCO, RefCOCO+, and RefCOCOg, shows the advantages of our methods for both referring expression generation and comprehension.