Can I read Osprey: Pixel Understanding with Visual Instruction Tuning on EtoBox?

Osprey: Pixel Understanding with Visual Instruction Tuning by Yuan, Yuqian; Li, Wentong; Liu, Jian; Tang, Dongqi; Luo, Xinjie; Qin, Chi; Zhang, Lei; Zhu, Jianke is a scholarly article available to read on EtoBox.

What is Osprey: Pixel Understanding with Visual Instruction Tuning about?

Multimodal large language models (MLLMs) have recently achieved impressive general-purpose vision-language capabilities through visual instruction tuning. However, current MLLMs primarily focus on image-level or box-level understanding, falling short in achieving fine-grained vision-language alignment at pixel level. Besides, the lack of mask-based instruction data limits their advancements. In this paper, we propose Osprey, a mask-text instruction tuning approach, to extend MLLMs by incorporating fine-grained mask regions into language instruction, aiming at achieving pixel-wise visual understanding. To achieve this goal, we first meticulously curate a mask-based region-text dataset with 724K samples, and then design a vision-language model by injecting pixel-level representation into LLM. Specifically, Osprey adopts a convolutional CLIP backbone as the vision encoder and employs a mask-aware visual extractor to extract precise visual mask features from high resolution input. Experimental results demonstrate Osprey's superiority in various region understanding tasks, showcasing its new capability for pixel-level instruction tuning. In particular, Osprey can be integrated with Segm

Author

Yuan, Yuqian; Li, Wentong; Liu, Jian; Tang, Dongqi; Luo, Xinjie; Qin, Chi; Zhang, Lei; Zhu, Jianke

Published

2023

Language

EN