31 citations · 83 across the 15 of their papers we have counts for
6 papers · 1 filter
PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions
Weifeng Lin, Xinyu Wei, Renrui Zhang +8
This paper presents a versatile image-to-image visual assistant, PixWizard, designed for image generation, manipulation, and translation based on free-from language instructions. T…
GUIOdyssey: A Comprehensive Dataset for Cross-App GUI Navigation on Mobile Devices
Quanfeng Lu, Wenqi Shao, Zitao Liu +7
Autonomous Graphical User Interface (GUI) navigation agents can enhance user experience in communication, entertainment, and productivity by streamlining workflows and reducing man…
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want
Weifeng Lin, Xinyu Wei, Ruichuan An +6
In this paper, we present the Draw-and-Understand framework, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs). V…
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
Dongyang Liu, Renrui Zhang, Longtian Qiu +16
We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX. To improve the architecture and training efficiency, we modify the SPHINX…
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models
Ziyi Lin, Chris Liu, Renrui Zhang +13
We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision-languag…
TinyLVLM-eHub: Towards Comprehensive and Efficient Evaluation for Large Vision-Language Models
Wenqi Shao, Meng Lei, Yutao Hu +8
Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated significant progress in tackling complex multimodal tasks. Among these cutting-edge developments, Goog…