ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
THE RESEARCH ATLAS
SAM 2: Segment Anything in Images and Videos
Extends promptable segmentation from images to video using streaming memory and interactive data collection.
Institution and location sources
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Why read this
Shows a transferable perception foundation; physical action still requires planning and control.
Citation
@misc{ravi2024sam,
title = {SAM 2: Segment Anything in Images and Videos},
author = {Nikhila Ravi and Valentin Gabeur and Yuan-Ting Hu and Ronghang Hu and Chaitanya Ryali and Tengyu Ma and Haitham Khedr and Roman Rädle and Chloe Rolland and Laura Gustafson and Eric Mintun and Junting Pan and Kalyan Vasudev Alwala and Nicolas Carion and Chao-Yuan Wu and Ross Girshick and Piotr Dollár and Christoph Feichtenhofer},
year = {2024},
eprint = {2408.00714},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2408.00714}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
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