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New MICo-150K dataset and benchmark advance multi-image composition tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MICo-150K, a large-scale dataset designed to improve multi-image composition (MICo) capabilities in AI models. The dataset addresses the challenge of synthesizing coherent images from multiple references by categorizing MICo into seven tasks and providing high-quality composite images. MICo-150K includes a unique subset for real-world image decomposition and recomposition, along with a benchmark suite and a new evaluation metric called Weighted-Ref-VIEScore. Fine-tuning models on this dataset has shown significant improvements in MICo tasks, with a baseline model, Qwen-MICo, demonstrating enhanced performance. AI

IMPACT Enhances AI's ability to generate complex images from multiple references, potentially improving creative tools and visual content generation.

RANK_REASON The cluster describes a new academic paper introducing a dataset and benchmark for multi-image composition.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MICo-150K dataset and benchmark advance multi-image composition tasks

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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xinyu Wei, Kangrui Cen, Hongyang Wei, Zhen Guo, Kai Cui, Bairui Li, Zeqing Wang, Jinrui Zhang, Lei Zhang ·

    MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition

    arXiv:2512.07348v2 Announce Type: replace Abstract: In controllable image generation, synthesizing coherent and consistent images from multiple reference inputs, i.e., Multi-Image Composition (MICo), remains a challenging problem, partly hindered by the lack of high-quality train…

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