Common Inpainted Objects In-N-Out of Context

• URL: http://arxiv.org/abs/2506.00721v1
• Date: Sat, 31 May 2025 21:42:12 GMT
• Title: Common Inpainted Objects In-N-Out of Context
• Authors: Tianze Yang, Tyson Jordan, Ninghao Liu, Jin Sun,
• Abstract summary: Common Inpainted Objects In-N-Out of Context (COinCO) is a novel dataset addressing the scarcity of out-of-context examples in existing vision datasets.<n>By systematically replacing objects in COCO images through diffusion-based inpainting, we create 97,722 unique images featuring both contextually coherent and inconsistent scenes.
• Score: 21.387506141979188
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Common Inpainted Objects In-N-Out of Context (COinCO), a novel dataset addressing the scarcity of out-of-context examples in existing vision datasets. By systematically replacing objects in COCO images through diffusion-based inpainting, we create 97,722 unique images featuring both contextually coherent and inconsistent scenes, enabling effective context learning. Each inpainted object is meticulously verified and categorized as in- or out-of-context through a multimodal large language model assessment. Our analysis reveals significant patterns in semantic priors that influence inpainting success across object categories. We demonstrate three key tasks enabled by COinCO: (1) training context classifiers that effectively determine whether existing objects belong in their context; (2) a novel Objects-from-Context prediction task that determines which new objects naturally belong in given scenes at both instance and clique levels, and (3) context-enhanced fake detection on state-of-the-art methods without fine-tuning. COinCO provides a controlled testbed with contextual variations, establishing a foundation for advancing context-aware visual understanding in computer vision and image forensics. Our code and data are at: https://github.com/YangTianze009/COinCO.

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