SAIR: Learning Semantic-aware Implicit Representation

• URL: http://arxiv.org/abs/2310.09285v1
• Date: Fri, 13 Oct 2023 17:52:16 GMT
• Title: SAIR: Learning Semantic-aware Implicit Representation
• Authors: Canyu Zhang, Xiaoguang Li, Qing Guo, Song Wang
• Abstract summary: Implicit representation of an image can map arbitrary coordinates in the continuous domain to their corresponding color values. Existing implicit representation approaches only focus on building continuous appearance mapping. We learn semantic-aware implicit representation (SAIR), that is, we make the implicit representation of each pixel rely on both its appearance and semantic information.
• Score: 23.842761556556216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Implicit representation of an image can map arbitrary coordinates in the continuous domain to their corresponding color values, presenting a powerful capability for image reconstruction. Nevertheless, existing implicit representation approaches only focus on building continuous appearance mapping, ignoring the continuities of the semantic information across pixels. As a result, they can hardly achieve desired reconstruction results when the semantic information within input images is corrupted, for example, a large region misses. To address the issue, we propose to learn semantic-aware implicit representation (SAIR), that is, we make the implicit representation of each pixel rely on both its appearance and semantic information (\eg, which object does the pixel belong to). To this end, we propose a framework with two modules: (1) building a semantic implicit representation (SIR) for a corrupted image whose large regions miss. Given an arbitrary coordinate in the continuous domain, we can obtain its respective text-aligned embedding indicating the object the pixel belongs. (2) building an appearance implicit representation (AIR) based on the SIR. Given an arbitrary coordinate in the continuous domain, we can reconstruct its color whether or not the pixel is missed in the input. We validate the novel semantic-aware implicit representation method on the image inpainting task, and the extensive experiments demonstrate that our method surpasses state-of-the-art approaches by a significant margin.

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