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object-detection

This is a list of awesome articles about object detection.

  • R-CNN
  • Fast R-CNN
  • Faster 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
  • MegNet
  • RefineNet
  • DetNet
  • SSOD
  • CornerNet
  • 3D Object Detection
  • ZSD（Zero-Shot Object Detection）
  • OSD（One-Shot object Detection）
  • 2018
  • Other

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

Papers&Codes

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

Fast R-CNN

Fast R-CNN

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

Faster R-CNN

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

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

Cascade R-CNN

Cascade R-CNN: Delving into High Quality Object Detection

SPP-Net

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

YOLO

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

img

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

img

YOLO: Core ML versus MPSNNGraph

TensorFlow YOLO object detection on Android

Computer Vision in iOS – Object Detection

YOLOv2

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

  • 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

Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras

OmniDetector: With Neural Networks to Bounding Boxes

YOLOv3

YOLOv3: An Incremental Improvement

YOLT

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

SSD

SSD: Single Shot MultiBox Detector

img

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

weiliu89/caffe#327

DSSD

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

FSSD: Feature Fusion Single Shot Multibox Detector

https://arxiv.org/abs/1712.00960

Weaving Multi-scale Context for Single Shot Detector

< 8000 ul dir="auto">
  • intro: WeaveNet
  • keywords: fuse multi-scale information
  • arxiv: https://arxiv.org/abs/1712.03149

    • ESSD

    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

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

    Pelee

    Pelee: A Real-Time Object Detection System on Mobile Devices

    https://github.com/Robert-JunWang/Pelee

    Fire SSD

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

    R-FCN

    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

    FPN

    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

    DSOD: Learning Deeply Supervised Object Detectors from Scratch

    img

    Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

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

    RetinaNet

    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

    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

    • keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation
    • arxiv: https://arxiv.org/abs/1712.08832

    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

    RefineNet

    Single-Shot Refinement Neural Network for Object Detection

    DetNet

    DetNet: A Backbone network for Object Detection

    SSOD

    Self-supervisory Signals for Object Discovery and Detection

    CornerNet

    CornerNet: Detecting Objects as Paired Keypoints

    3D Object Detection

    LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs

    ZSD

    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

    OSD

    One-Shot Object Detection

    RepMet: Representative-based metric learning for classification and one-shot object detection

    2018

    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

    Other

    R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos

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    Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

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