amusi/awesome-object-detection

object-detection

[TOC] This is a list of awesome articles about object detection. If you want to read the paper according to time, you can refer to Date.

• R-CNN
• Fast R-CNN
• Faster R-CNN
• Mask R-CNN
• Light-Head R-CNN
• Cascade R-CNN
• SPP-Net
• YOLO
• YOLOv2
• YOLOv3
• YOLT
• SSD
• DSSD
• FSSD
• ESSD
• MDSSD
• Pelee
• Fire SSD
• R-FCN
• FPN
• DSOD
• RetinaNet
• MegDet
• RefineNet
• DetNet
• SSOD
• CornerNet
• M2Det
• 3D Object Detection
• ZSD（Zero-Shot Object Detection）
• OSD（One-Shot object Detection）
• Weakly Supervised Object Detection
• Softer-NMS
• 2018
• 2019
• Other

Based on handong1587's github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

Survey

Imbalance Problems in Object Detection: A Review

• intro: under review at TPAMI
• arXiv:

Recent Advances in Deep Learning for Object Detection

• intro: From 2013 (OverFeat) to 2019 (DetNAS)
• arXiv:

A Survey of Deep Learning-based Object Detection

• intro：From Fast R-CNN to NAS-FPN
• arXiv：

Object Detection in 20 Years: A Survey

• intro：This work has been submitted to the IEEE TPAMI for possible publication
• arXiv：

《Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks》

• intro: awesome
• arXiv: https://arxiv.org/abs/1809.03193

《Deep Learning for Generic Object Detection: A Survey》

• intro: Submitted to IJCV 2018
• arXiv: https://arxiv.org/abs/1809.02165

Papers&Codes

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

• intro: R-CNN
• arxiv:
• supp:
• slides:
• slides:
• github:
• notes:
• caffe-pr("Make R-CNN the Caffe detection example"):

Fast R-CNN

Fast R-CNN

• arxiv:
• slides:
• github:
• github(COCO-branch):
• webcam demo:
• notes:
• notes:
• github("Fast R-CNN in MXNet"):
• github:
• github:
• github:

A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection

• intro: CVPR 2017
• arxiv:
• paper:
• github(Caffe):

Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

• intro: NIPS 2015
• arxiv:
• gitxiv:
• slides:
• github(official, Matlab):
• github(Caffe):
• github(MXNet):
• github(PyTorch--recommend):
• github:
• github(Torch)::
• github(Torch)::
• github(TensorFlow):
• github(TensorFlow):
• github(C++ demo):
• github(Keras):
• github:
• github(C++):

R-CNN minus R

• intro: BMVC 2015
• arxiv:

Faster R-CNN in MXNet with distributed implementation and data parallelization

• github:

Contextual Priming and Feedback for Faster R-CNN

• intro: ECCV 2016. Carnegie Mellon University
• paper:
• poster:

An Implementation of Faster RCNN with Study for Region Sampling

• intro: Technical Report, 3 pages. CMU
• arxiv:
• github:
• github: https://github.com/ruotianluo/pytorch-faster-rcnn

Interpretable R-CNN

• intro: North Carolina State University & Alibaba
• keywords: AND-OR Graph (AOG)
• arxiv:

Domain Adaptive Faster R-CNN for Object Detection in the Wild

• intro: CVPR 2018. ETH Zurich & ESAT/PSI
• arxiv:

Mask R-CNN

• arxiv:
• github(Keras): https://github.com/matterport/Mask_RCNN
• github(Caffe2): https://github.com/facebookresearch/Detectron
• github(Pytorch):
• github(MXNet): https://github.com/TuSimple/mx-maskrcnn
• github(Chainer): https://github.com/DeNA/Chainer_Mask_R-CNN

Light-Head R-CNN

Light-Head R-CNN: In Defense of Two-Stage Object Detector

• intro: Tsinghua University & Megvii Inc
• arxiv:
• github(offical): https://github.com/zengarden/light_head_rcnn
• github:

Cascade R-CNN

Cascade R-CNN: Delving into High Quality Object Detection

• arxiv:
• github:

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

• intro: ECCV 2014 / TPAMI 2015
• arxiv:
• github:
• notes:

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

• intro: PAMI 2016
• intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations
• project page:
• arxiv:

Object Detectors Emerge in Deep Scene CNNs

• intro: ICLR 2015
• arxiv:
• paper:
• paper:
• slides:

segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

• intro: CVPR 2015
• project(code+data):
• arxiv:
• github:

Object Detection Networks on Convolutional Feature Maps

• intro: TPAMI 2015
• keywords: NoC
• arxiv:

Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

• arxiv:
• slides:
• github:

DeepBox: Learning Objectness with Convolutional Networks

• keywords: DeepBox
• arxiv:
• github:

YOLO

You Only Look Once: Unified, Real-Time Object Detection

• arxiv:
• code:
• github:
• blog:
• slides:
• reddit:
• github:
• github:
• github:
• github:
• github:
• github:
• github:
• github:

darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++

• blog:
• github:

Start Training YOLO with Our Own Data

• intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
• blog:
• github:

YOLO: Core ML versus MPSNNGraph

• intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
• blog:
• github:

TensorFlow YOLO object detection on Android

• intro: Real-time object detection on Android using the YOLO network with TensorFlow
• github:

Computer Vision in iOS – Object Detection

• blog:
• github:

YOLOv2

YOLO9000: Better, Faster, Stronger

• arxiv:
• code: https://pjreddie.com/darknet/yolov2/
• github(Chainer):
• github(Keras):
• github(PyTorch):
• github(Tensorflow):
• github(Windows):
• github:
• github:
• github(TensorFlow):
• github(Keras):
• github(Keras):
• github(TensorFlow):

darknet_scripts

• intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
• github:

Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2

• github:

LightNet: Bringing pjreddie's DarkNet out of the shadows YOLO v2 Bounding Box Tool

• intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
• github:

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

• intro: LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.
• arxiv: https://arxiv.org/abs/1804.04606

Object detection at 200 Frames Per Second

• intro: faster than Tiny-Yolo-v2
• arxiv: https://arxiv.org/abs/1805.06361

Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras

• intro: YOLE--Object Detection in Neuromorphic Cameras
• arxiv:https://arxiv.org/abs/1805.07931

OmniDetector: With Neural Networks to Bounding Boxes

• intro: a person detector on n fish-eye images of indoor scenes（NIPS 2018）
• arxiv:https://arxiv.org/abs/1805.08503
• datasets:https://gitlab.com/omnidetector/omnidetector

YOLOv3

YOLOv3: An Incremental Improvement

• arxiv:https://arxiv.org/abs/1804.02767
• paper:https://pjreddie.com/media/files/papers/YOLOv3.pdf
• code:
• github(Official):https://github.com/pjreddie/darknet
• github:https://github.com/mystic123/tensorflow-yolo-v3
• github:https://github.com/experiencor/keras-yolo3
• github:https://github.com/qqwweee/keras-yolo3
• github:https://github.com/marvis/pytorch-yolo3
• github:https://github.com/ayooshkathuria/pytorch-yolo-v3
• github:https://github.com/ayooshkathuria/YOLO_v3_tutorial_from_scratch
• github:https://github.com/eriklindernoren/PyTorch-YOLOv3
• github:https://github.com/ultralytics/yolov3
• github:https://github.com/BobLiu20/YOLOv3_PyTorch
• github:https://github.com/andy-yun/pytorch-0.4-yolov3
• github:https://github.com/DeNA/PyTorch_YOLOv3

YOLT

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

• intro: Small Object Detection
• arxiv:https://arxiv.org/abs/1805.09512
• github:https://github.com/avanetten/yolt

SSD

SSD: Single Shot MultiBox Detector

• intro: ECCV 2016 Oral
• arxiv:
• paper:
• slides: http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.pdf
• github(Official):
• video:
• github:
• github:
• github:
• github:
• github:
• github(Caffe):

What's the diffience in performance between this new code you pushed and the previous code? #327

DSSD

DSSD : Deconvolutional Single Shot Detector

• intro: UNC Chapel Hill & Amazon Inc
• arxiv:
• github:
• github:
• demo:

Enhancement of SSD by concatenating feature maps for object detection

• intro: rainbow SSD (R-SSD)
• arxiv:

Context-aware Single-Shot Detector

• keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
• arxiv:

Feature-Fused SSD: Fast Detection for Small Objects

FSSD

FSSD: Feature Fusion Single Shot Multibox Detector Weaving Multi-scale Context for Single Shot Detector

• intro: WeaveNet
• keywords: fuse multi-scale information
• arxiv:

ESSD

Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection

MDSSD

MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects

• arxiv: https://arxiv.org/abs/1805.07009

Pelee

Pelee: A Real-Time Object Detection System on Mobile Devices https://github.com/Robert-JunWang/Pelee

• intro: (ICLR 2018 workshop track)
• arxiv: https://arxiv.org/abs/1804.06882
• github: https://github.com/Robert-JunWang/Pelee

Fire SSD

Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device

• intro:low cost, fast speed and high mAP on factor edge computing devices
• arxiv:https://arxiv.org/abs/1806.05363

R-FCN

R-FCN: Object Detection via Region-based Fully Convolutional Networks

• arxiv:
• github:
• github(MXNet):
• github:
• github:
• github:
• github:

R-FCN-3000 at 30fps: Decoupling Detection and Classification Recycle deep features for better object detection

• arxiv:

FPN

Feature Pyramid Networks for Object Detection

• intro: Facebook AI Research
• arxiv:

Action-Driven Object Detection with Top-Down Visual Attentions

• arxiv:

Beyond Skip Connections: Top-Down Modulation for Object Detection

• intro: CMU & UC Berkeley & Google Research
• arxiv:

Wide-Residual-Inception Networks for Real-time Object Detection

• intro: Inha University
• arxiv:

Attentional Network for Visual Object Detection

• intro: University of Maryland & Mitsubishi Electric Research Laboratories
• arxiv:

Learning Chained Deep Features and Classifiers for Cascade in Object Detection

• keykwords: CC-Net
• intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007
• arxiv:

DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling

• intro: ICCV 2017 (poster)
• arxiv:

Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries

• intro: CVPR 2017
• arxiv:

Spatial Memory for Context Reasoning in Object Detection

• arxiv:

Accurate Single Stage Detector Using Recurrent Rolling Convolution

• intro: CVPR 2017. SenseTime
• keywords: Recurrent Rolling Convolution (RRC)
• arxiv:
• github:

Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems

• intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc
• arxiv:

Point Linking Network for Object Detection

• intro: Point Linking Network (PLN)
• arxiv:

Perceptual Generative Adversarial Networks for Small Object Detection Few-shot Object Detection Yes-Net: An effective Detector Based on Global Information SMC Faster R-CNN: Toward a scene-specialized multi-object detector Towards lightweight convolutional neural networks for object detection RON: Reverse Connection with Objectness Prior Networks for Object Detection

• intro: CVPR 2017
• arxiv:
• github:

Mimicking Very Efficient Network for Object Detection

• intro: CVPR 2017. SenseTime & Beihang University
• paper:

Residual Features and Unified Prediction Network for Single Stage Detection Deformable Part-based Fully Convolutional Network for Object Detection

• intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
• arxiv:

Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors

• intro: ICCV 2017
• arxiv:

Recurrent Scale Approximation for Object Detection in CNN

• intro: ICCV 2017
• keywords: Recurrent Scale Approximation (RSA)
• arxiv:
• github:

DSOD

DSOD: Learning Deeply Supervised Object Detectors from Scratch

• intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
• arxiv:
• github:
• github:https://github.com/Windaway/DSOD-Tensorflow
• github:https://github.com/chenyuntc/dsod.pytorch

Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

• arxiv:https://arxiv.org/abs/1712.00886
• github:https://github.com/szq0214/GRP-DSOD

Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages

• intro: BMVC 2018
• arXiv: https://arxiv.org/abs/1807.11013

Object Detection from Scratch with Deep Supervision

• intro: This is an extended version of DSOD
• arXiv: https://arxiv.org/abs/1809.09294

RetinaNet

Focal Loss for Dense Object Detection

• intro: ICCV 2017 Best student paper award. Facebook AI Research
• keywords: RetinaNet
• arxiv:

**CoupleNet: Coupling Global Structu