ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
Documents a driving dataset and applications for sensor fusion, semantic understanding, and localization.
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Trace the perception and data foundations that precede today's vision-language-action driving research.
@misc{huang2018the,
title = {The ApolloScape Open Dataset for Autonomous Driving and its Application},
author = {Xinyu Huang and Peng Wang and Xinjing Cheng and Dingfu Zhou and Qichuan Geng and Ruigang Yang},
year = {2018},
eprint = {1803.06184},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/1803.06184}
}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.