Summary of Retrieval-guided Cross-view Image Synthesis, by Hongji Yang et al.
Retrieval-guided Cross-view Image Synthesis
by Hongji Yang, Yiru Li, Yingying Zhu
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework that leverages information retrieval techniques to facilitate effective cross-view image synthesis. The authors introduce a retrieval-guided approach that captures semantic similarities across different viewpoints through contrastive learning, creating a smooth embedding space. This framework is trained without relying on auxiliary information such as semantic segmentation maps or preprocessing modules. Furthermore, the authors introduce a novel fusion mechanism that leverages these embeddings to guide image synthesis while learning and encoding both view-invariant and view-specific features. The proposed approach is evaluated on three datasets: CVUSA, CVACT, and VIGOR-GEN, showing significant improvements in retrieval accuracy (R@1) and synthesis quality (FID). This work bridges the gap between information retrieval and synthesis tasks, offering insights into how retrieval techniques can address complex cross-domain synthesis challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer algorithms to help create new images from different viewpoints. It’s like taking a photo of a building from one angle, and then trying to create a similar image from a completely different angle. The authors developed a new way to do this that uses techniques normally used for searching through large amounts of text or data. They tested their method on three big datasets and found that it worked much better than other methods in creating accurate and realistic images. |
Keywords
» Artificial intelligence » Embedding space » Image synthesis » Semantic segmentation
