基于深度卷积神经网络的 ImageNet 分类
ImageNet Classification with Deep Convolutional Neural Networks
深度卷积网络将数据、GPU 计算与表示学习结合，为现代视觉研究提供了重要起点。
THE RESEARCH ATLAS
Qwen3-VL 技术报告
Qwen 视觉语言家族在不同规模模型中融合文本、图像与视频理解。
Qwen3-VL Technical Report
为什么值得读
将多模态数据设计与纯语言训练对照阅读。
引用
@misc{bai2025qwen3,
title = {Qwen3-VL Technical Report},
author = {Shuai Bai and Yuxuan Cai and Ruizhe Chen and Keqin Chen and Xionghui Chen and Zesen Cheng and Lianghao Deng and Wei Ding and Chang Gao and Chunjiang Ge and Wenbin Ge and Zhifang Guo and Qidong Huang and Jie Huang and Fei Huang and Binyuan Hui and Shutong Jiang and Zhaohai Li and Mingsheng Li and Mei Li and Kaixin Li and Zicheng Lin and Junyang Lin and Xuejing Liu and Jiawei Liu and Chenglong Liu and Yang Liu and Dayiheng Liu and Shixuan Liu and Dunjie Lu and Ruilin Luo and Chenxu Lv and Rui Men and Lingchen Meng and Xuancheng Ren and Xingzhang Ren and Sibo Song and Yuchong Sun and Jun Tang and Jianhong Tu and Jianqiang Wan and Peng Wang and Pengfei Wang and Qiuyue Wang and Yuxuan Wang and Tianbao Xie and Yiheng Xu and Haiyang Xu and Jin Xu and Zhibo Yang and Mingkun Yang and Jianxin Yang and An Yang and Bowen Yu and Fei Zhang and Hang Zhang and Xi Zhang and Bo Zheng and Humen Zhong and Jingren Zhou and Fan Zhou and Jing Zhou and Yuanzhi Zhu and Ke Zhu},
year = {2025},
eprint = {2511.21631},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2511.21631}
}论文与代码版权归原作者；引用及许可请以原始来源为准。
相关研究
注意力即全部
Attention Is All You Need
Transformer 用注意力机制构建序列模型，成为后续语言与多模态模型的重要基础。
BERT：面向语言理解的深度双向 Transformer 预训练
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
通过掩码语言预训练学习双向表示，再迁移到语言理解任务。