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。
开源视觉—语言—动作模型及适配框架,为通用机器人策略研究提供基础。
OpenVLA: An Open-Source Vision-Language-Action Model
适合研究机器人数据、动作 Token 化与实际微调流程。
@misc{kim2024openvla,
title = {OpenVLA: An Open-Source Vision-Language-Action Model},
author = {Moo Jin Kim and Karl Pertsch and Siddharth Karamcheti and Ted Xiao and Ashwin Balakrishna and Suraj Nair and Rafael Rafailov and Ethan Foster and Grace Lam and Pannag Sanketi and Quan Vuong and Thomas Kollar and Benjamin Burchfiel and Russ Tedrake and Dorsa Sadigh and Sergey Levine and Percy Liang and Chelsea Finn},
year = {2024},
eprint = {2406.09246},
archivePrefix = {arXiv},
primaryClass = {cs.RO},
url = {https://arxiv.org/abs/2406.09246}
}论文与代码版权归原作者;引用及许可请以原始来源为准。
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。
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。
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。