Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: YOLOv 3 : An Incremental Improvement
YOLO9000: Better, Faster, Stronger
Soft-NMS — Improving Object Detection with One Line of Code
Fast R-CNN
Accelerating Very Deep Convolutional Networks for Classification and Detection
A real-time multi-class multi-object tracker using YOLOv2
towards incremental learning with deep convolutional networks .
Deep Neural Networks for Object Detection
DeepID-Net: Deformable deep convolutional neural networks for object detection
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Scalable, High-Quality Object Detection
Scalable Object Detection Using Deep Neural Networks
Object Detection With Deep Learning: A Review
Gradually Updated Neural Networks for Large-Scale Image Recognition
MegDet: A Large Mini-Batch Object Detector
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
Object Detection using Deep Learning
DSOD: Learning Deeply Supervised Object Detectors from Scratch
A Survey of Modern Object Detection Literature using Deep Learning
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
Mimicking Very Efficient Network for Object Detection
Capacitated Dynamic Programming: Faster Knapsack and Graph Algorithms
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
Multi-dimensional analysis of embedded systems security
Universal Dependency Parser : A Single Parser for Many Languages on Arc-Swift
Big data as a promoter of industry 4.0: Lessons of the semiconductor industry