ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
An open vision-language-action model and adaptation framework make generalist robot policy research more accessible.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Useful for studying robot data, action tokenization and practical fine-tuning.
@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}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
A generative model learns to reverse a gradual noise process, establishing an influential approach to image synthesis.
Contrastive training aligns images with natural-language descriptions, enabling transfer to new visual categories.