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
Qwen-RobotWorld：语言驱动的具身世界模型
语言条件视频世界模型预测操作、导航和驾驶等场景中的未来视觉轨迹。
Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation
为什么值得读
区分对未来画面的预测与在真实物理环境中验证的控制表现。
引用
@misc{zhang2026qwen,
title = {Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation},
author = {Jie Zhang and Xiaoyue Chen and Anzhe Chen and Dayiheng Liu and Deqing Li and Gengze Zhou and Hale Yin and Haoqi Yuan and Haoyang Li and Jiahao Li and Jiazhao Zhang and Jingren Zhou and Kaiyuan Gao and Kun Yan and Lihan Jiang and Ningyuan Tang and Pei Lin and Qihang Peng and Shengming Yin and Tianhe Wu and Tianyi Yan and Xiao Xu and Yan Shu and Yanran Zhang and Ye Wang and Yi Wang and Yilei Chen and Yixian Xu and Yiyang Huang and Yuxiang Chen and Zekai Zhang and Zhendong Wang and Zixing Lei and Zhixuan Liang and Zihao Liu and Zikai Zhou and Chenxu Lv and Xiong-Hui Chen and Chenfei Wu},
year = {2026},
eprint = {2606.17030},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2606.17030}
}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
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Denoising Diffusion Probabilistic Models
模型学习逐步反转加噪过程，为图像生成建立了重要的扩散建模路线。
从自然语言监督中学习可迁移的视觉模型
Learning Transferable Visual Models From Natural Language Supervision
通过对比学习对齐图像与自然语言描述，使视觉表示能够迁移到新类别。