Summary
A computer vision paper titled 'From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Image Generation' by Xingjian Wang, Zhao Wang, and Taihang Hu, posted on arXiv as 2608.18076 and shared on zhichai.net. The work addresses large-scale image generation, which has traditionally benefited from advances in data scale, quality, rebalancing, and recaptioning. Instead of treating training corpora as a monolithic resource, the paper proposes a capability-centric approach to data design, organizing and curating data around the specific generation capabilities a model needs to acquire, so that data curation and model capabilities co-evolve during development. The post includes the paper metadata, a Chinese summary, and a link to the arXiv abstract page. Full methodological details, experimental results, and benchmarks are available in the original paper.
Paper Overview
Field: Computer Vision (CV)
Authors: Xingjian Wang, Zhao Wang, Taihang Hu
Posted: 2026-08-18
arXiv: 2608.18076
Summary
Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning. This paper proposes shifting from corpus-level data curation to a capability-centric data design paradigm, in which training data is organized around the specific generation capabilities a model should acquire, allowing data design and model capabilities to co-evolve.
Original Abstract (excerpt)
Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning...
For the full paper, see the arXiv page.
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