Context-Aware Captions From Context-Agnostic Supervision

Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 251-260

Abstract


We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation). For example, given images and captions of "siamese cat" and "tiger cat", we generate language that describes the "siamese cat" in a way that distinguishes it from "tiger cat". Our key novelty is that we show how to do joint inference over a language model that is context-agnostic and a listener which distinguishes closely-related concepts. We first apply our technique to a justification task, namely to describe why an image contains a particular fine-grained category as opposed to another closely-related category of the CUB- 200-2011 dataset. We then study discriminative image captioning to generate language that uniquely refers to one of two semantically-similar images in the COCO dataset. Evaluations with discriminative ground truth for justification and human studies for discriminative image captioning reveal that our approach outperforms baseline generative and speaker-listener approaches for discrimination.

Related Material


[pdf] [arXiv] [video]
[bibtex]
@InProceedings{Vedantam_2017_CVPR,
author = {Vedantam, Ramakrishna and Bengio, Samy and Murphy, Kevin and Parikh, Devi and Chechik, Gal},
title = {Context-Aware Captions From Context-Agnostic Supervision},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}