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20232026
most citedMMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning

18 citations · 29 across the 28 of their papers we have counts for

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8 papers · 1 filter

cs.CV2026

Crafter: A Multi-Agent Harness for Editable Scientific Figure Generation from Diverse Inputs

Haozhe Zhao, Shuzheng Si, Zhenhailong Wang +6

Scientific figures are among the most effective means of communicating complex research ideas, yet producing publication-quality illustrations remains one of the most labor-intensi…

cs.CV2025

MENTOR: Efficient Multimodal-Conditioned Tuning for Autoregressive Vision Generation Models

Haozhe Zhao, Zefan Cai, Shuzheng Si +5

Recent text-to-image models produce high-quality results but still struggle with precise visual control, balancing multimodal inputs, and requiring extensive training for complex m…

cs.CV2025

Multimodal Representation Alignment for Image Generation: Text-Image Interleaved Control Is Easier Than You Think

Liang Chen, Shuai Bai, Wenhao Chai +5

The field of advanced text-to-image generation is witnessing the emergence of unified frameworks that integrate powerful text encoders, such as CLIP and T5, with Diffusion Transfor…

cs.CV2024

Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance

Haozhe Zhao, Shuzheng Si, Liang Chen +4

Large vision-language models (LVLMs) have achieved impressive results in various vision-language tasks. However, despite showing promising performance, LVLMs suffer from hallucinat…

cs.CV2024

A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation

Liang Chen, Sinan Tan, Zefan Cai +7

This work tackles the information loss bottleneck of vector-quantization (VQ) autoregressive image generation by introducing a novel model architecture called the 2-Dimensional Aut…

cs.CV2024★ 1 cited

UltraEdit: Instruction-based Fine-Grained Image Editing at Scale

Haozhe Zhao, Xiaojian Ma, Liang Chen +7

This paper presents UltraEdit, a large-scale (approximately 4 million editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to addr…