Self-supervised Photographic Image Layout Representation Learning

• URL: http://arxiv.org/abs/2403.03740v2
• Date: Tue, 20 Aug 2024 17:05:13 GMT
• Title: Self-supervised Photographic Image Layout Representation Learning
• Authors: Zhaoran Zhao, Peng Lu, Xujun Peng, Wenhao Guo,
• Abstract summary: We develop an autoencoder-based network architecture skilled in compressing heterogeneous layout graphs into precise, dimensionally-reduced layout representations. We introduce the LODB dataset, which features a broader range of layout categories and richer semantics. Our extensive experimentation on this dataset demonstrates the superior performance of our approach in the realm of photographic image layout representation learning.
• Score: 5.009120058742792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the domain of image layout representation learning, the critical process of translating image layouts into succinct vector forms is increasingly significant across diverse applications, such as image retrieval, manipulation, and generation. Most approaches in this area heavily rely on costly labeled datasets and notably lack in adapting their modeling and learning methods to the specific nuances of photographic image layouts. This shortfall makes the learning process for photographic image layouts suboptimal. In our research, we directly address these challenges. We innovate by defining basic layout primitives that encapsulate various levels of layout information and by mapping these, along with their interconnections, onto a heterogeneous graph structure. This graph is meticulously engineered to capture the intricate layout information within the pixel domain explicitly. Advancing further, we introduce novel pretext tasks coupled with customized loss functions, strategically designed for effective self-supervised learning of these layout graphs. Building on this foundation, we develop an autoencoder-based network architecture skilled in compressing these heterogeneous layout graphs into precise, dimensionally-reduced layout representations. Additionally, we introduce the LODB dataset, which features a broader range of layout categories and richer semantics, serving as a comprehensive benchmark for evaluating the effectiveness of layout representation learning methods. Our extensive experimentation on this dataset demonstrates the superior performance of our approach in the realm of photographic image layout representation learning.

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