Modeling Context in Referring Expressions
Published in
European Conference on Computer Vision(2016)External Links:
TL
TL;DR
This work focuses on incorporating better measures of visual context into referring expression models and finds that visual comparison to other objects within an image helps improve performance significantly.
Abstract
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 (Datasets and toolbox can be downloaded from https://github.com/lichengunc/refer), shows the advantages of our methods for both referring expression generation and comprehension.
References41 items
1
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
2
Deep Residual Learning for Image Recognition
3
SSD: Single Shot MultiBox Detector
4
DenseCap: Fully Convolutional Localization Networks for Dense Captioning
5