ImageNet Classification with Deep Convolutional Neural Networks"
by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton (2012)

This paper introduced the deep convolutional neural network (CNN)
called AlexNet, which achieved a breakthrough in the ImageNet
challenge, revolutionizing the field of computer vision.
"Mask R-CNN" by Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross
B. Girshick (2017)

This paper introduces Mask R-CNN, an extension of Faster R-CNN for
object detection and instance segmentation. It has become one of the
most cited papers in modern computer vision.
"YOLO: You Only Look Once" by Joseph Redmon, Santosh Divvala,
Ross B. Girshick, and Ali Farhadi (2016)

This paper presents the YOLO (You Only Look Once) algorithm, a
fast and efficient method for real-time object detection.
"U-Net: Convolutional Networks for Biomedical Image
Segmentation" by Olaf Ronneberger, Philipp Fischer, and Thomas Brox
(2015)

This paper proposes U-Net, a CNN architecture for semantic
segmentation in medical images. It has been widely used for
segmentation tasks across various domains.
"SIFT: Scale-Invariant Feature Transform" by David Lowe (1999)

This classic paper introduced SIFT, an algorithm for detecting and
describing local features in images, which has been a cornerstone for
tasks like object recognition and matching.
"Deep Learning for Computer Vision" by Rajalingappaa
Shanmugamani (2017)

This book provides a comprehensive introduction to computer vision
using deep learning techniques. It's helpful for both beginners and
those looking to delve deeper into applications.