Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
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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.
Based on handong1587's github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
Imbalance Problems in Object Detection: A Review
Recent Advances in Deep Learning for Object Detection
A Survey of Deep Learning-based Object Detection
intro:From Fast R-CNN to NAS-FPN
Object Detection in 20 Years: A Survey
《Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks》
intro: awesome
《Deep Learning for Generic Object Detection: A Survey》
Rich feature hierarchies for accurate object detection and semantic segmentation
Fast R-CNN
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
R-CNN minus R
Faster R-CNN in MXNet with distributed implementation and data parallelization
Contextual Priming and Feedback for Faster R-CNN
An Implementation of Faster RCNN with Study for Region Sampling
Interpretable R-CNN
Domain Adaptive Faster R-CNN for Object Detection in the Wild
Light-Head R-CNN: In Defense of Two-Stage Object Detector
Cascade R-CNN: Delving into High Quality Object Detection
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
Object Detectors Emerge in Deep Scene CNNs
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
Object Detection Networks on Convolutional Feature Maps
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
DeepBox: Learning Objectness with Convolutional Networks
You Only Look Once: Unified, Real-Time Object Detection
darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
Start Training YOLO with Our Own Data
YOLO: Core ML versus MPSNNGraph
TensorFlow YOLO object detection on Android
Computer Vision in iOS – Object Detection
YOLO9000: Better, Faster, Stronger
darknet_scripts
Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
LightNet: Bringing pjreddie's DarkNet out of the shadows
https://github.com//explosion/lightnet
YOLO v2 Bounding Box Tool
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
Object detection at 200 Frames Per Second
Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras
OmniDetector: With Neural Networks to Bounding Boxes
YOLOv3: An Incremental Improvement
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: Single Shot MultiBox Detector
What's the diffience in performance between this new code you pushed and the previous code? #327
https://github.com/weiliu89/caffe/issues/327
DSSD : Deconvolutional Single Shot Detector
Enhancement of SSD by concatenating feature maps for object detection
Context-aware Single-Shot Detector
Feature-Fused SSD: Fast Detection for Small Objects
https://arxiv.org/abs/1709.05054
FSSD: Feature Fusion Single Shot Multibox Detector
https://arxiv.org/abs/1712.00960
Weaving Multi-scale Context for Single Shot Detector
Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
https://arxiv.org/abs/1801.05918
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
https://arxiv.org/abs/1802.06488
MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects
Pelee: A Real-Time Object Detection System on Mobile Devices
https://github.com/Robert-JunWang/Pelee
intro: (ICLR 2018 workshop track)
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: Object Detection via Region-based Fully Convolutional Networks
R-FCN-3000 at 30fps: Decoupling Detection and Classification
https://arxiv.org/abs/1712.01802
Recycle deep features for better object detection
Feature Pyramid Networks for Object Detection
Action-Driven Object Detection with Top-Down Visual Attentions
Beyond Skip Connections: Top-Down Modulation for Object Detection
Wide-Residual-Inception Networks for Real-time Object Detection
Attentional Network for Visual Object Detection
Learning Chained Deep Features and Classifiers for Cascade in Object Detection
DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
Spatial Memory for Context Reasoning in Object Detection
Accurate Single Stage Detector Using Recurrent Rolling Convolution
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
https://arxiv.org/abs/1704.05775
LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems
Point Linking Network for Object Detection
Perceptual Generative Adversarial Networks for Small Object Detection
https://arxiv.org/abs/1706.05274
Few-shot Object Detection
https://arxiv.org/abs/1706.08249
Yes-Net: An effective Detector Based on Global Information
https://arxiv.org/abs/1706.09180
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
https://arxiv.org/abs/1706.10217
Towards lightweight convolutional neural networks for object detection
https://arxiv.org/abs/1707.01395
RON: Reverse Connection with Objectness Prior Networks for Object Detection
Mimicking Very Efficient Network for Object Detection
Residual Features and Unified Prediction Network for Single Stage Detection
https://arxiv.org/abs/1707.05031
Deformable Part-based Fully Convolutional Network for Object Detection
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
Recurrent Scale Approximation for Object Detection in CNN
DSOD: Learning Deeply Supervised Object Detectors from Scratch
Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
Object Detection from Scratch with Deep Supervision
Focal Loss for Dense Object Detection
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
Incremental Learning of Object Detectors without Catastrophic Forgetting
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
https://arxiv.org/abs/1709.04347
StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
https://arxiv.org/abs/1709.05788
Dynamic Zoom-in Network for Fast Object Detection in Large Images
https://arxiv.org/abs/1711.05187
Zero-Annotation Object Detection with Web Knowledge Transfer
MegDet: A Large Mini-Batch Object Detector
Receptive Field Block Net for Accurate and Fast Object Detection
An Analysis of Scale Invariance in Object Detection - SNIP
Feature Selective Networks for Object Detection
https://arxiv.org/abs/1711.08879
Learning a Rotation Invariant Detector with Rotatable Bounding Box
Scalable Object Detection for Stylized Objects
Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
Deep Regionlets for Object Detection
Training and Testing Object Detectors with Virtual Images
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
Spot the Difference by Object Detection
Localization-Aware Active Learning for Object Detection
Object Detection with Mask-based Feature Encoding
LSTD: A Low-Shot Transfer Detector for Object Detection
Pseudo Mask Augmented Object Detection
https://arxiv.org/abs/1803.05858
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
https://arxiv.org/abs/1803.06799
Learning Region Features for Object Detection
Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
Object Detection for Comics using Manga109 Annotations
Task-Driven Super Resolution: Object Detection in Low-resolution Images
Transferring Common-Sense Knowledge for Object Detection
Multi-scale Location-aware Kernel Representation for Object Detection
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
Robust Physical Adversarial Attack on Faster R-CNN Object Detector
Single-Shot Refinement Neural Network for Object Detection
intro: CVPR 2018
DetNet: A Backbone network for Object Detection
Self-supervisory Signals for Object Discovery and Detection
CornerNet: Detecting Objects as Paired Keypoints
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
3D Backbone Network for 3D Object Detection
LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs
Zero-Shot Detection
Zero-Shot Object Detection
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
Zero-Shot Object Detection by Hybrid Region Embedding
Comparison Network for One-Shot Conditional Object Detection
One-Shot Object Detection
RepMet: Representative-based metric learning for classification and one-shot object detection
Weakly Supervised Object Detection in Artworks
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
《Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection》
Feature Selective Anchor-Free Module for Single-Shot Object Detection
intro: CVPR 2019
Object Detection based on Region Decomposition and Assembly
intro: AAAI 2019
Bottom-up Object Detection by Grouping Extreme and Center Points
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features
intro: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Consistent Optimization for Single-Shot Object Detection
intro: improves RetinaNet from 39.1 AP to 40.1 AP on COCO datase
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
Region Proposal by Guided Anchoring
Scale-Aware Trident Networks for Object Detection
Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions
Strong-Weak Distribution Alignment for Adaptive Object Detection
AutoFocus: Efficient Multi-Scale Inference
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection
Grid R-CNN
Deformable ConvNets v2: More Deformable, Better Results
intro: Microsoft Research Asia
Anchor Box Optimization for Object Detection
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
Learning RoI Transformer for Detecting Oriented Objects in Aerial Images
Integrated Object Detection and Tracking with Tracklet-Conditioned Detection
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection
Gradient Harmonized Single-stage Detector
CFENet: Object Detection with Comprehensive Feature Enhancement Module
DeRPN: Taking a further step toward more general object detection
Hybrid Knowledge Routed Modules for Large-scale Object Detection
《Receptive Field Block Net for Accurate and Fast Object Detection》
Deep Feature Pyramid Reconfiguration for Object Detection
Unsupervised Hard Example Mining from Videos for Improved Object Detection
Acquisition of Localization Confidence for Accurate Object Detection
Toward Scale-Invariance and Position-Sensitive Region Proposal Networks
MetaAnchor: Learning to Detect Objects with Customized Anchors
Relation Network for Object Detection
Quantization Mimic: Towards Very Tiny CNN for Object Detection
Learning Rich Features for Image Manipulation Detection
SNIPER: Efficient Multi-Scale Training
Soft Sampling for Robust Object Detection
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos