Summary of Deepseek-vl2: Mixture-of-experts Vision-language Models For Advanced Multimodal Understanding, by Zhiyu Wu et al.

DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

by Zhiyu Wu, Xiaokang Chen, Zizheng Pan, Xingchao Liu, Wen Liu, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan

First submitted to arxiv on: 13 Dec 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary We present DeepSeek-VL2, a series of advanced Mixture-of-Experts (MoE) Vision-Language Models that improves upon its predecessor, DeepSeek-VL. The model incorporates a dynamic tiling vision encoding strategy for processing high-resolution images with different aspect ratios and leverages DeepSeekMoE models with the Multi-head Latent Attention mechanism for efficient inference and high throughput. Trained on an improved vision-language dataset, DeepSeek-VL2 demonstrates superior capabilities across various tasks, including visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. The model series consists of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small, and DeepSeek-VL2, with 1.0B, 2.8B, and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models.
Low	GrooveSquid.com (original content)	Low Difficulty Summary We’ve developed a new AI model that can understand images and text better than before. It’s called DeepSeek-VL2, and it’s made up of two parts: one for seeing and one for understanding words. The “seeing” part is good at handling different-sized images, and the “understanding” part uses a special trick to quickly figure out what’s important. We trained this model on a big dataset and tested it on many tasks like answering questions about pictures, recognizing text in old documents, and understanding tables and charts. Our model comes in three sizes, and it can do things that other models can’t do as well or even better with fewer “brain cells” than they have.