Bottom-up Modeling of Repeated Elements via Single Image Analysis-by-Synthesis
We address the problem of discovering repeated elements from a single image. In contrast to existing approaches that depend on large annotated datasets, curated multi-image collections, or object segmentation masks, we show that a single image can suffice to learn a meaningful object model in a completely bottom-up fashion, without any prior knowledge beyond a coarse scale prior. Our method learns a tunable image-space prototype of the repeated elements through a reconstruction objective, enabling the model to identify and synthesize consistent object instances within the same image. Experiments on 116 real images from the FSC-147 dataset demonstrate that our method successfully learns co...
| Authors | Syrine Kalleli, Alexei A. Efros, Mathieu Aubry |
|---|---|
| Primary category | cs.CV |
| Published | 7 September 2026 |
| Last updated at source | 16 September 2026 |
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