Arxiv Paper - Qwen2-VL: Enhancing Vision-Language Model’s Perception of the World at Any Resolution

In this episode, we discuss Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution by Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, Junyang Lin. The Qwen2-VL Series introduces Naive Dynamic Resolution for processing images of varying resolutions more efficiently and integrates Multimodal Rotary Position Embedding for improved fusion of positional information across modalities. It employs a unified approach for both images and videos, enhancing visual perception and explores scaling laws for large vision-language models by increasing model size and training data. The Qwen2-VL-72B model achieves competitive performance, rivaling top models like GPT-4o and Claude3.5-Sonnet, and surpasses other generalist models across various benchmarks.