Computer Vision
1.R-CNN Paper Review - The Beginning of the Region-Based Object Detection
Summary Region-based Convolutional Neural Network(R-CNN) is a foundational model in object detection.
2.Fast R-CNN Paper Review - Innovations in Region-Based Object Detection
Fast R-CNN simplifies object detection with innovations like RoI Pooling, enabling faster computation and efficient feature sharing.
3.Faster R-CNN Paper Review - An End-to-End Solution for Efficient Object Detection
Discover how Faster R-CNN unifies region proposals and classification for near real-time object detection.
4.Oriented R-CNN for Object Detection Paper Review - Advancing Rotated Object Detection
The Oriented R-CNN model aims to detect not only the location of objects but also ther orientations. It improves existing two-stage object detector
5.VGGNet Paper Review - Very Deep Convolutional Networks for Large-Scale Image Recognition
VGGNet is a deep convolutional neural network designed to improve image classification performance by increasing network depth while using small conv
6.ResNet Paper Review - Deep Residual Learning for Image Recognition
ResNet Paper Review
7.Feature Pyramid Network Paper Review - Enhancing Object Detection with Feature Pyramids
The Feature Pyramid Network (FPN) is designed to enhance multi-scale object detection by leveraging both high-level and low-level semantic features.
8.DETR Paper Review - End-to-End Object Detection with Transformers
DETR PAPER REVIEW - adopting Transformer to object detection