8 citations · 12 across the 7 of their papers we have counts for
3 papers · 1 filter
DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing
Zhaokai Wang, Mingxin Liu, Zirun Zhu +11
Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a knowledge-intensive diagram whose co…
GRADE: Benchmarking Discipline-Informed Reasoning in Image Editing
Mingxin Liu, Ziqian Fan, Zhaokai Wang +13
Unified multimodal models target joint understanding, reasoning, and generation, but current image editing benchmarks are largely confined to natural images and shallow commonsense…
InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing
Changyao Tian, Danni Yang, Guanzhou Chen +26
Unified multimodal models (UMMs) that integrate understanding, reasoning, generation, and editing face inherent trade-offs between maintaining strong semantic comprehension and acq…