Reproduce “Osprey: Pixel Understanding with Visual Instruction Tuning” on OpenResearch
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About this paper
Yuqian Yuan, Wentong Li, Jian Liu, Dongqi Tang, Xinjie Luo, Chi Qin, Lei Zhang, Jianke Zhu
Osprey, a collaborative effort involving Zhejiang University and Microsoft, introduces a framework for multimodal large language models to achieve precise pixel-level visual understanding through mask-based instruction tuning. It establishes new performance benchmarks in tasks like open-vocabulary segmentation and referring object classification by utilizing a newly curated, large-scale mask-text dataset.
Key insights
- Convolutional vision encoders demonstrate superior performance for high-resolution, fine-grained tasks compared to ViT-based architectures in MLLMs.
- High-quality, mask-based instruction tuning data, including negative samples for robustness and short-form prompts for flexibility, is essential for advancing pixel-level understanding.
- Integrating multi-level features and spatial information from precise mask regions into the language model significantly enhances fine-grained vision-language alignment.
Summary by alphaXiv.
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