@inproceedings{chen2017show,
author = {Chen, Tseng-Hung and Fu, Jianlong and Sun, Min},
title = {Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner},
booktitle = {IEEE International Conference on Computer Vision (ICCV)},
year = {2017},
month = {October},
abstract = {Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel adversarial training procedure to leverage unpaired data in the target domain. Two critic networks are introduced to guide the captioner, namely domain critic and multi-modal critic. The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain. The multi-modal critic assesses whether an image and its generated sentence are a valid pair. During training, the critics and captioner act as adversaries – captioner aims to generate indistinguishable sentences, whereas critics aim at distinguishing them. The assessment improves the captioner through policy gradient updates. During inference, we further propose a novel critic-based planning method to select high-quality sentences without additional supervision (e.g.,tags). To evaluate, we use MSCOCO as the source domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k) as the target domains. Our method consistently performs well on all datasets. In particular, on CUB-200-2011, we achieve 21.8% CIDEr-D improvement after adaptation. Utilizing critics during inference further givesanother4.5%boost.

 },
url = {http://approjects.co.za/?big=en-us/research/publication/show-adapt-tell-adversarial-training-cross-domain-image-captioner/},
edition = {IEEE International Conference on Computer Vision (ICCV)},
}