Diverse Semantic Image Synthesis with various conditioning modalities

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chaoyue Wu , Rui Li , Cheng Liu , Si Wu , Hau-San Wong
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引用次数: 0

Abstract

Semantic image synthesis aims to generate high-fidelity images from a segmentation mask, and previous methods typically train a generator to associate a global random map with the conditioning mask. However, the lack of independent control of regional content impedes their application. To address this issue, we propose an effective approach for Multi-modal conditioning-based Diverse Semantic Image Synthesis, which is referred to as McDSIS. In this model, there are a number of constituent generators incorporated to synthesize the content in semantic regions from independent random maps. The regional content can be determined by the style code associated with a random map, extracted from a reference image, or by embedding a textual description via our proposed conditioning mechanisms. As a result, the generation process is spatially disentangled, which facilitates independent synthesis of diverse content in a semantic region, while at the same time preserving other content. Due to this flexible architecture, in addition to achieving superior performance over state-of-the-art semantic image generation models, McDSIS is capable of performing various visual tasks, such as face inpainting, swapping, local editing, etc.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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