Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing

Autor:	Zhao, Liuqing, Lyu, Fan, Hu, Fuyuan, Huang, Kaizhu, Xu, Fenglei, Li, Linyan
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Computer Science - Multimedia
Druh dokumentu:	Working Paper
Popis:	Sentence-based Image Editing (SIE) aims to deploy natural language to edit an image. Offering potentials to reduce expensive manual editing, SIE has attracted much interest recently. However, existing methods can hardly produce accurate editing and even lead to failures in attribute editing when the query sentence is with multiple editable attributes. To cope with this problem, by focusing on enhancing the difference between attributes, this paper proposes a novel model called Contrastive Attention Generative Adversarial Network (CA-GAN), which is inspired from contrastive training. Specifically, we first design a novel contrastive attention module to enlarge the editing difference between random combinations of attributes which are formed during training. We then construct an attribute discriminator to ensure effective editing on each attribute. A series of experiments show that our method can generate very encouraging results in sentence-based image editing with multiple attributes on CUB and COCO dataset. Our code is available at https://github.com/Zlq2021/CA-GAN Comment: Accepted by BMVC 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2110.11159 Zobrazit plný text záznamu View this record from Arxiv