Object Detection(目标检测神文)----2
papers
损失函数
[CVPR2019] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
- arxiv: https://arxiv.org/abs/1902.09630
Deep Neural Networks for Object Detection
- paper: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
- arxiv: http://arxiv.org/abs/1312.6229
- github: https://github.com/sermanet/OverFeat
- code: http://cilvr.nyu.edu/doku.php?id=software:overfeat:start
R-CNN
Rich feature hierarchies for accurate object detection and semantic segmentation
- intro: R-CNN
- arxiv: http://arxiv.org/abs/1311.2524
- supp: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf
- slides: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
- slides: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
- github: https://github.com/rbgirshick/rcnn
- notes: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
- caffe-pr(“Make R-CNN the Caffe detection example”): https://github.com/BVLC/caffe/pull/482
Fast R-CNN
Fast R-CNN
- arxiv: http://arxiv.org/abs/1504.08083
- slides: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
- github: https://github.com/rbgirshick/fast-rcnn
- github(COCO-branch): https://github.com/rbgirshick/fast-rcnn/tree/coco
- webcam demo: https://github.com/rbgirshick/fast-rcnn/pull/29
- notes: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
- notes: http://blog.csdn.net/linj_m/article/details/48930179
- github(“Fast R-CNN in MXNet”): https://github.com/precedenceguo/mx-rcnn
- github: https://github.com/mahyarnajibi/fast-rcnn-torch
- github: https://github.com/apple2373/chainer-simple-fast-rnn
- github: https://github.com/zplizzi/tensorflow-fast-rcnn
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1704.03414
- paper: http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf
- github(Caffe): https://github.com/xiaolonw/adversarial-frcnn
Faster R-CNN
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- intro: NIPS 2015
- arxiv: http://arxiv.org/abs/1506.01497
- gitxiv: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region
- slides: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf
- github(official, Matlab): https://github.com/ShaoqingRen/faster_rcnn
- github: https://github.com/rbgirshick/py-faster-rcnn
- github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn
- github: https://github.com//jwyang/faster-rcnn.pytorch
- github: https://github.com/mitmul/chainer-faster-rcnn
- github: https://github.com/andreaskoepf/faster-rcnn.torch
- github: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch
- github: https://github.com/smallcorgi/Faster-RCNN_TF
- github: https://github.com/CharlesShang/TFFRCNN
- github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus
- github: https://github.com/yhenon/keras-frcnn
- github: https://github.com/Eniac-Xie/faster-rcnn-resnet
- github(C++): https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev
R-CNN minus R
- intro: BMVC 2015
- arxiv: http://arxiv.org/abs/1506.06981
Faster R-CNN in MXNet with distributed implementation and data parallelization
- github: https://github.com/dmlc/mxnet/tree/master/example/rcnn
Contextual Priming and Feedback for Faster R-CNN
- intro: ECCV 2016. Carnegie Mellon University
- paper: http://abhinavsh.info/context_priming_feedback.pdf
- poster: http://www.eccv2016.org/files/posters/P-1A-20.pdf
An Implementation of Faster RCNN with Study for Region Sampling
- intro: Technical Report, 3 pages. CMU
- arxiv: https://arxiv.org/abs/1702.02138
- github: https://github.com/endernewton/tf-faster-rcnn
Interpretable R-CNN
- intro: North Carolina State University & Alibaba
- keywords: AND-OR Graph (AOG)
- arxiv: https://arxiv.org/abs/1711.05226
[AAAI2019]Object Detection based on Region Decomposition and Assembly
- intro: AAAI2019,区域分解组装
- arxiv: https://arxiv.org/abs/1901.08225
- translate: https://zhuanlan.zhihu.com/p/58951221 论文翻译
Light-Head R-CNN
Light-Head R-CNN: In Defense of Two-Stage Object Detector
- intro: Tsinghua University & Megvii Inc
- arxiv: https://arxiv.org/abs/1711.07264
- github(official, Tensorflow): https://github.com/zengarden/light_head_rcnn
- github: https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet_v1_101_rfcn_light.py#L784
##Cascade R-CNN
Cascade R-CNN: Delving into High Quality Object Detection
- intro: CVPR 2018. UC San Diego
- arxiv: https://arxiv.org/abs/1712.00726
- github(Caffe, official): https://github.com/zhaoweicai/cascade-rcnn
MultiBox
Scalable Object Detection using Deep Neural Networks
- intro: first MultiBox. Train a CNN to predict Region of Interest.
- arxiv: http://arxiv.org/abs/1312.2249
- github: https://github.com/google/multibox
- blog: https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html
Scalable, High-Quality Object Detection
- intro: second MultiBox
- arxiv: http://arxiv.org/abs/1412.1441
- github: https://github.com/google/multibox
SPP-Net
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- intro: ECCV 2014 / TPAMI 2015
- arxiv: http://arxiv.org/abs/1406.4729
- github: https://github.com/ShaoqingRen/SPP_net
- notes: http://zhangliliang.com/2014/09/13/paper-note-sppnet/
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: http://www.ee.cuhk.edu.hk/˜wlouyang/projects/imagenetDeepId/index.html
- arxiv: http://arxiv.org/abs/1412.5661
Object Detectors Emerge in Deep Scene CNNs
- intro: ICLR 2015
- arxiv: http://arxiv.org/abs/1412.6856
- paper: https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf
- paper: https://people.csail.mit.edu/khosla/papers/iclr2015_zhou.pdf
- slides: http://places.csail.mit.edu/slide_iclr2015.pdf
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
- intro: CVPR 2015
- project(code+data): https://www.cs.toronto.edu/~yukun/segdeepm.html
- arxiv: https://arxiv.org/abs/1502.04275
- github: https://github.com/YknZhu/segDeepM
Object Detection Networks on Convolutional Feature Maps
- intro: TPAMI 2015
- keywords: NoC
- arxiv: http://arxiv.org/abs/1504.06066
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
- arxiv: http://arxiv.org/abs/1504.03293
- slides: http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf
- github: https://github.com/YutingZhang/fgs-obj
DeepBox: Learning Objectness with Convolutional Networks
- keywords: DeepBox
- arxiv: http://arxiv.org/abs/1505.02146
- github: https://github.com/weichengkuo/DeepBox
MR-CNN
Object detection via a multi-region & semantic segmentation-aware CNN model
- intro: ICCV 2015. MR-CNN
- arxiv: http://arxiv.org/abs/1505.01749
- github: https://github.com/gidariss/mrcnn-object-detection
- notes: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
- notes: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/
YOLO
You Only Look Once: Unified, Real-Time Object Detection
- arxiv: http://arxiv.org/abs/1506.02640
- code: http://pjreddie.com/darknet/yolo/
- github: https://github.com/pjreddie/darknet
- blog: https://pjreddie.com/publications/yolo/
- slides: https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p
- reddit: https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/
- github: https://github.com/gliese581gg/YOLO_tensorflow
- github: https://github.com/xingwangsfu/caffe-yolo
- github: https://github.com/frankzhangrui/Darknet-Yolo
- github: https://github.com/BriSkyHekun/py-darknet-yolo
- github: https://github.com/tommy-qichang/yolo.torch
- github: https://github.com/frischzenger/yolo-windows
- github: https://github.com/AlexeyAB/yolo-windows
- github: https://github.com/nilboy/tensorflow-yolo
darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
- blog: https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp
- github: https://github.com/thtrieu/darkflow
Start Training YOLO with Our Own Data
- intro: train with customized data and class numbers/labels. Linux / Windows version for darknet.
- blog: http://guanghan.info/blog/en/my-works/train-yolo/
- github: https://github.com/Guanghan/darknet
YOLO: Core ML versus MPSNNGraph
- intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
- blog: http://machinethink.net/blog/yolo-coreml-versus-mps-graph/
- github: https://github.com/hollance/YOLO-CoreML-MPSNNGraph
TensorFlow YOLO object detection on Android
- intro: Real-time object detection on Android using the YOLO network with TensorFlow
- github: https://github.com/natanielruiz/android-yolo
Computer Vision in iOS – Object Detection
- blog: https://sriraghu.com/2017/07/12/computer-vision-in-ios-object-detection/
- github:https://github.com/r4ghu/iOS-CoreML-Yolo
YOLOv2
YOLO9000: Better, Faster, Stronger
- arxiv: https://arxiv.org/abs/1612.08242
- code: http://pjreddie.com/yolo9000/
- github(Chainer): https://github.com/leetenki/YOLOv2
- github(Keras): https://github.com/allanzelener/YAD2K
- github(PyTorch): https://github.com/longcw/yolo2-pytorch
- github(Tensorflow): https://github.com/hizhangp/yolo_tensorflow
- github(Windows): https://github.com/AlexeyAB/darknet
- github: https://github.com/choasUp/caffe-yolo9000
- github: https://github.com/philipperemy/yolo-9000
darknet_scripts
- intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors?
- github: https://github.com/Jumabek/darknet_scripts
Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2
- github: https://github.com/AlexeyAB/Yolo_mark
LightNet: Bringing pjreddie’s DarkNet out of the shadows
- github: https://github.com//explosion/lightnet
YOLO v2 Bounding Box Tool
- intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires.
- github: https://github.com/Cartucho/yolo-boundingbox-labeler-GUI
YOLOv3
YOLOv3: An Incremental Improvement
- project page: https://pjreddie.com/darknet/yolo/
- arxiv: https://arxiv.org/abs/1804.02767
- github: https://github.com/DeNA/PyTorch_YOLOv3
- github: https://github.com/eriklindernoren/PyTorch-YOLOv3
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers
- arxiv：https://arxiv.org/abs/1811.05588
AttentionNet: Aggregating Weak Directions for Accurate Object Detection
- intro: ICCV 2015
- intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection task
- arxiv: http://arxiv.org/abs/1506.07704
- slides: https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf
- slides: http://image-net.org/challenges/talks/lunit-kaist-slide.pdf
DenseBox
DenseBox: Unifying Landmark Localization with End to End Object Detection
- arxiv: http://arxiv.org/abs/1509.04874
- demo: http://pan.baidu.com/s/1mgoWWsS
- KITTI result: http://www.cvlibs.net/datasets/kitti/eval_object.php
SSD
SSD: Single Shot MultiBox Detector
- intro: ECCV 2016 Oral
- arxiv: http://arxiv.org/abs/1512.02325
- paper: http://www.cs.unc.edu/~wliu/papers/ssd.pdf
- slides: http://www.cs.unc.edu/~wliu/papers/ssd_eccv2016_slide.pdf
- github(Official): https://github.com/weiliu89/caffe/tree/ssd
- video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973
- github: https://github.com/zhreshold/mxnet-ssd
- github: https://github.com/zhreshold/mxnet-ssd.cpp
- github: https://github.com/rykov8/ssd_keras
- github: https://github.com/balancap/SSD-Tensorflow
- github: https://github.com/amdegroot/ssd.pytorch
- github(Caffe): https://github.com/chuanqi305/MobileNet-SSD
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
DSSD : Deconvolutional Single Shot Detector
- intro: UNC Chapel Hill & Amazon Inc
- arxiv: https://arxiv.org/abs/1701.06659
- github: https://github.com/chengyangfu/caffe/tree/dssd
- github: https://github.com/MTCloudVision/mxnet-dssd
- demo: http://120.52.72.53/www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd_lalaland.mp4
Enhancement of SSD by concatenating feature maps for object detection
- intro: rainbow SSD (R-SSD)
- arxiv: https://arxiv.org/abs/1705.09587
Context-aware Single-Shot Detector
- keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)
- arxiv: https://arxiv.org/abs/1707.08682
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
- 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
- arxiv: https://arxiv.org/abs/1801.05918
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
- arxiv: https://arxiv.org/abs/1802.06488
MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects
- intro: Zhengzhou University
- arxiv: https://arxiv.org/abs/1805.07009
Inside-Outside Net (ION)
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
- intro: “0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it”.
- arxiv: http://arxiv.org/abs/1512.04143
- slides: http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf
- coco-leaderboard: http://mscoco.org/dataset/#detections-leaderboard
Adaptive Object Detection Using Adjacency and Zoom Prediction
- intro: CVPR 2016. AZ-Net
- arxiv: http://arxiv.org/abs/1512.07711
- github: https://github.com/luyongxi/az-net
- youtube: https://www.youtube.com/watch?v=YmFtuNwxaNM
G-CNN: an Iterative Grid Based Object Detector
- arxiv: http://arxiv.org/abs/1512.07729
Factors in Finetuning Deep Model for object detection
Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution
- intro: CVPR 2016.rank 3rd for provided data and 2nd for external data on ILSVRC 2015 object detection
- project page: http://www.ee.cuhk.edu.hk/~wlouyang/projects/ImageNetFactors/CVPR16.html
- arxiv: http://arxiv.org/abs/1601.05150
We don’t need no bounding-boxes: Training object class detectors using only human verification
- arxiv: http://arxiv.org/abs/1602.08405
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
- arxiv: http://arxiv.org/abs/1604.00600
A MultiPath Network for Object Detection
- intro: BMVC 2016. Facebook AI Research (FAIR)
- arxiv: http://arxiv.org/abs/1604.02135
- github: https://github.com/facebookresearch/multipathnet
CRAFT
CRAFT Objects from Images
- intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN
- project page: http://byangderek.github.io/projects/craft.html
- arxiv: https://arxiv.org/abs/1604.03239
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_CRAFT_Objects_From_CVPR_2016_paper.pdf
- github: https://github.com/byangderek/CRAFT
OHEM
Training Region-based Object Detectors with Online Hard Example Mining
- intro: CVPR 2016 Oral. Online hard example mining (OHEM)
- arxiv: http://arxiv.org/abs/1604.03540
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.pdf
- github(Official): https://github.com/abhi2610/ohem
- author page: http://abhinav-shrivastava.info/
S-OHEM: Stratified Online Hard Example Mining for Object Detection
- arxiv: https://arxiv.org/abs/1705.02233
Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers
- intro: CVPR 2016
- keywords: scale-dependent pooling (SDP), cascaded rejection classifiers (CRC)
- paper: http://www-personal.umich.edu/~wgchoi/SDP-CRC_camready.pdf
R-FCN
R-FCN: Object Detection via Region-based Fully Convolutional Networks
arxiv: http://arxiv.org/abs/1605.06409
github: https://github.com/daijifeng001/R-FCN
github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn
github: https://github.com/Orpine/py-R-FCN
github: https://github.com/PureDiors/pytorch_RFCN
github: https://github.com/bharatsingh430/py-R-FCN-multiGPU
github: https://github.com/xdever/RFCN-tensorflow
R-FCN-3000 at 30fps: Decoupling Detection and Classification
- arxiv: https://arxiv.org/abs/1712.01802
Recycle deep features for better object detection
- arxiv: http://arxiv.org/abs/1607.05066
MS-CNN
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
- intro: ECCV 2016
- intro: 640×480: 15 fps, 960×720: 8 fps
- arxiv: http://arxiv.org/abs/1607.07155
- github: https://github.com/zhaoweicai/mscnn
- poster: http://www.eccv2016.org/files/posters/P-2B-38.pdf
Multi-stage Object Detection with Group Recursive Learning
- intro: VOC2007: 78.6%, VOC2012: 74.9%
- arxiv: http://arxiv.org/abs/1608.05159
Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection
- intro: WACV 2017. SubCNN
- arxiv: http://arxiv.org/abs/1604.04693
- github: https://github.com/tanshen/SubCNN
PVANET
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
- intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). Continuation of arXiv:1608.08021
- arxiv: https://arxiv.org/abs/1611.08588
- github: https://github.com/sanghoon/pva-faster-rcnn
- leaderboard(PVANet 9.0): http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4
GBD-Net
Gated Bi-directional CNN for Object Detection
- intro: The Chinese University of Hong Kong & Sensetime Group Limited
- paper: http://link.springer.com/chapter/10.1007/978-3-319-46478-7_22
- mirror: https://pan.baidu.com/s/1dFohO7v
Crafting GBD-Net for Object Detection
- intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo
- intro: gated bi-directional CNN (GBD-Net)
- arxiv: https://arxiv.org/abs/1610.02579
- github: https://github.com/craftGBD/craftGBD
StuffNet: Using ‘Stuff’ to Improve Object Detection
- arxiv: https://arxiv.org/abs/1610.05861
Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene
- arxiv: https://arxiv.org/abs/1610.09609
Hierarchical Object Detection with Deep Reinforcement Learning
- intro: Deep Reinforcement Learning Workshop (NIPS 2016)
- project page: https://imatge-upc.github.io/detection-2016-nipsws/
- arxiv: https://arxiv.org/abs/1611.03718
- slides: http://www.slideshare.net/xavigiro/hierarchical-object-detection-with-deep-reinforcement-learning
- github: https://github.com/imatge-upc/detection-2016-nipsws
- blog: http://jorditorres.org/nips/
Learning to detect and localize many objects from few examples
- arxiv: https://arxiv.org/abs/1611.05664
Speed/accuracy trade-offs for modern convolutional object detectors
- intro: CVPR 2017. Google Research
- arxiv: https://arxiv.org/abs/1611.10012
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
- arxiv: https://arxiv.org/abs/1612.01051
- github: https://github.com/BichenWuUCB/squeezeDet
- github: https://github.com/fregu856/2D_detection
Feature Pyramid Network (FPN)
Feature Pyramid Networks for Object Detection
- intro: Facebook AI Research
- arxiv: https://arxiv.org/abs/1612.03144
Action-Driven Object Detection with Top-Down Visual Attentions
- arxiv: https://arxiv.org/abs/1612.06704
Beyond Skip Connections: Top-Down Modulation for Object Detection
- intro: CMU & UC Berkeley & Google Research
- arxiv: https://arxiv.org/abs/1612.06851
Wide-Residual-Inception Networks for Real-time Object Detection
- intro: Inha University
- arxiv: https://arxiv.org/abs/1702.01243
Attentional Network for Visual Object Detection
- intro: University of Maryland & Mitsubishi Electric Research Laboratories
- arxiv: https://arxiv.org/abs/1702.01478
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: https://arxiv.org/abs/1702.07054
DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling
- intro: ICCV 2017 (poster)
- arxiv: https://arxiv.org/abs/1703.10295
Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1704.03944
Spatial Memory for Context Reasoning in Object Detection
- arxiv: https://arxiv.org/abs/1704.04224
Accurate Single Stage Detector Using Recurrent Rolling Convolution
- intro: CVPR 2017. SenseTime
- keywords: Recurrent Rolling Convolution (RRC)
- arxiv: https://arxiv.org/abs/1704.05776
- github: https://github.com/xiaohaoChen/rrc_detection
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
- arxiv: https://arxiv.org/abs/1704.05775
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: https://arxiv.org/abs/1705.05922
Point Linking Network for Object Detection
- intro: Point Linking Network (PLN)
- arxiv: https://arxiv.org/abs/1706.03646
Perceptual Generative Adversarial Networks for Small Object Detection
- arxiv: https://arxiv.org/abs/1706.05274
Few-shot Object Detection
- arxiv: https://arxiv.org/abs/1706.08249
Yes-Net: An effective Detector Based on Global Information
- arxiv: https://arxiv.org/abs/1706.09180
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
- arxiv: https://arxiv.org/abs/1706.10217
Towards lightweight convolutional neural networks for object detection
- arxiv: https://arxiv.org/abs/1707.01395
RON: Reverse Connection with Objectness Prior Networks for Object Detection
- intro: CVPR 2017
- arxiv: https://arxiv.org/abs/1707.01691
- github: https://github.com/taokong/RON
Mimicking Very Efficient Network for Object Detection
- intro: CVPR 2017. SenseTime & Beihang University
- paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf
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
- intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC
- arxiv: https://arxiv.org/abs/1707.06175
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1707.06399
Recurrent Scale Approximation for Object Detection in CNN
- intro: ICCV 2017
- keywords: Recurrent Scale Approximation (RSA)
- arxiv: https://arxiv.org/abs/1707.09531
- github: https://github.com/sciencefans/RSA-for-object-detection
DSOD
DSOD: Learning Deeply Supervised Object Detectors from Scratch
- intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China
- arxiv: https://arxiv.org/abs/1708.01241
- github: https://github.com/szq0214/DSOD
Object Detection from Scratch with Deep Supervision
- 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: https://arxiv.org/abs/1708.02002
Focal Loss Dense Detector for Vehicle Surveillance
- arxiv: https://arxiv.org/abs/1803.01114
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1708.02863
Incremental Learning of Object Detectors without Catastrophic Forgetting
- intro: ICCV 2017. Inria
- arxiv: https://arxiv.org/abs/1708.06977
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
- arxiv: https://arxiv.org/abs/1709.04347
StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
- arxiv: 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
- intro: NTU, Singapore & Amazon
- keywords: multi-instance multi-label domain adaption learning framework
- arxiv: https://arxiv.org/abs/1711.05954
MegDet
MegDet: A Large Mini-Batch Object Detector
- intro: Peking University & Tsinghua University & Megvii Inc
- arxiv: https://arxiv.org/abs/1711.07240
Single-Shot Refinement Neural Network for Object Detection
- arxiv: https://arxiv.org/abs/1711.06897
- github: https://github.com/sfzhang15/RefineDet
- github: https://github.com/MTCloudVision/RefineDet-Mxnet
Receptive Field Block Net for Accurate and Fast Object Detection
- intro: RFBNet
- arxiv: https://arxiv.org/abs/1711.07767
- github: https://github.com//ruinmessi/RFBNet
An Analysis of Scale Invariance in Object Detection - SNIP
- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1711.08189
- github: https://github.com/bharatsingh430/snip
Feature Selective Networks for Object Detection
- arxiv: https://arxiv.org/abs/1711.08879
Learning a Rotation Invariant Detector with Rotatable Bounding Box
- arxiv: https://arxiv.org/abs/1711.09405
- github(official, Caffe): https://github.com/liulei01/DRBox
Scalable Object Detection for Stylized Objects
- intro: Microsoft AI & Research Munich
- arxiv: https://arxiv.org/abs/1711.09822
Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids
- arxiv: https://arxiv.org/abs/1712.00886
- github: https://github.com/szq0214/GRP-DSOD
Deep Regionlets for Object Detection
- keywords: region selection network, gating network
- arxiv: https://arxiv.org/abs/1712.02408
Training and Testing Object Detectors with Virtual Images
- intro: IEEE/CAA Journal of Automatica Sinica
- arxiv: https://arxiv.org/abs/1712.08470
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
- intro: Tsinghua University & JD Group
- arxiv: https://arxiv.org/abs/1801.01051
Localization-Aware Active Learning for Object Detection
- arxiv: https://arxiv.org/abs/1801.05124
Object Detection with Mask-based Feature Encoding
- arxiv: https://arxiv.org/abs/1802.03934
LSTD: A Low-Shot Transfer Detector for Object Detection
- intro: AAAI 2018
- arxiv: https://arxiv.org/abs/1803.01529
Domain Adaptive Faster R-CNN for Object Detection in the Wild
- intro: CVPR 2018. ETH Zurich & ESAT/PSI
- arxiv: https://arxiv.org/abs/1803.03243
- github(official. Caffe): https://github.com/yuhuayc/da-faster-rcnn
Pseudo Mask Augmented Object Detection
- arxiv: https://arxiv.org/abs/1803.05858
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
- intro: ECCV 2018
- keywords: DCR V1
- arxiv: https://arxiv.org/abs/1803.06799
- github(official, MXNet): https://github.com/bowenc0221/Decoupled-Classification-Refinement
Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
- keywords: DCR V2
- arxiv: https://arxiv.org/abs/1810.04002
- github(official, MXNet): https://github.com/bowenc0221/Decoupled-Classification-Refinement
Learning Region Features for Object Detection
- intro: Peking University & MSRA
- arxiv: https://arxiv.org/abs/1803.07066
Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
- intro: Singapore Management University & Zhejiang University
- arxiv: https://arxiv.org/abs/1803.08208
Object Detection for Comics using Manga109 Annotations
- intro: University of Tokyo & National Institute of Informatics, Japan
- arxiv: https://arxiv.org/abs/1803.08670
Task-Driven Super Resolution: Object Detection in Low-resolution Images
- arxiv: https://arxiv.org/abs/1803.11316
Transferring Common-Sense Knowledge for Object Detection
- arxiv: https://arxiv.org/abs/1804.01077
Multi-scale Location-aware Kernel Representation for Object Detection
- intro: CVPR 2018
- arxiv: https://arxiv.org/abs/1804.00428
- github: https://github.com/Hwang64/MLKP
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
- intro: National University of Defense Technology
- arxiv: https://arxiv.org/abs/1804.04606
DetNet: A Backbone network for Object Detection
- intro: Tsinghua University & Megvii Inc
- arxiv: https://arxiv.org/abs/1804.06215
Robust Physical Adversarial Attack on Faster R-CNN Object Detector
- arxiv: https://arxiv.org/abs/1804.05810
AdvDetPatch: Attacking Object Detectors with Adversarial Patches
- arxiv: https://arxiv.org/abs/1806.02299
Attacking Object Detectors via Imperceptible Patches on Background
- https://arxiv.org/abs/1809.05966
Physical Adversarial Examples for Object Detectors
- intro: WOOT 2018
- arxiv: https://arxiv.org/abs/1807.07769
Quantization Mimic: Towards Very Tiny CNN for Object Detection
- arxiv: https://arxiv.org/abs/1805.02152
Object detection at 200 Frames Per Second
- intro: United Technologies Research Center-Ireland
- arxiv: https://arxiv.org/abs/1805.06361
Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images
- intro: CVPR 2018 Deep Vision Workshop
- arxiv: https://arxiv.org/abs/1805.11778
SNIPER: Efficient Multi-Scale Training
- intro: University of Maryland
- keywords: SNIPER (Scale Normalization for Image Pyramid with Efficient Resampling)
- arxiv: https://arxiv.org/abs/1805.09300
- github: https://github.com/mahyarnajibi/SNIPER
Soft Sampling for Robust Object Detection
- arxiv: https://arxiv.org/abs/1806.06986
MetaAnchor: Learning to Detect Objects with Customized Anchors
- intro: Megvii Inc (Face++) & Fudan University
- arxiv: https://arxiv.org/abs/1807.00980
Localization Recall Precision (LRP): A New Performance Metric for Object Detection
- intro: ECCV 2018. Middle East Technical University
- arxiv: https://arxiv.org/abs/1807.01696
- github: https://github.com/cancam/LRP
Auto-Context R-CNN
- intro: Rejected by ECCV18
- arxiv: https://arxiv.org/abs/1807.02842
Pooling Pyramid Network for Object Detection
- intro: Google AI Perception
- arxiv: https://arxiv.org/abs/1807.03284
Modeling Visual Context is Key to Augmenting Object Detection Datasets
- intro: ECCV 2018
- arxiv: https://arxiv.org/abs/1807.07428
Dual Refinement Network for Single-Shot Object Detection
- arxiv: https://arxiv.org/abs/1807.08638
Acquisition of Localization Confidence for Accurate Object Detection
- intro: ECCV 2018
- arxiv: https://arxiv.org/abs/1807.11590
- gihtub: https://github.com/vacancy/PreciseRoIPooling
CornerNet: Detecting Objects as Paired Keypoints
- intro: ECCV 2018
- keywords: IoU-Net, PreciseRoIPooling
- arxiv: https://arxiv.org/abs/1808.01244
- github: https://github.com/umich-vl/CornerNet
Unsupervised Hard Example Mining from Videos for Improved Object Detection
- intro: ECCV 2018
- arxiv: https://arxiv.org/abs/1808.04285
SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection
- arxiv: https://arxiv.org/abs/1808.04974
A Survey of Modern Object Detection Literature using Deep Learning
- arxiv: https://arxiv.org/abs/1808.07256
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages
- intro: BMVC 2018
- arxiv: https://arxiv.org/abs/1807.11013
- github: https://github.com/lyxok1/Tiny-DSOD
Deep Feature Pyramid Reconfiguration for Object Detection
- intro: ECCV 2018
- arxiv: https://arxiv.org/abs/1808.07993
MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection
- intro: ICPR 2018
- arxiv: https://arxiv.org/abs/1809.01791
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
- https://arxiv.org/abs/1809.03193
Deep Learning for Generic Object Detection: A Survey
- https://arxiv.org/abs/1809.02165
Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples
- intro: ICLR 2018
- arxiv: https://github.com/alinlab/Confident_classifier
ScratchDet:Exploring to Train Single-Shot Object Detectors from Scratch
- arxiv: https://arxiv.org/abs/1810.08425
- github: https://github.com/KimSoybean/ScratchDet
Fast and accurate object detection in high resolution 4K and 8K video using GPUs
- intro: Best Paper Finalist at IEEE High Performance Extreme Computing Conference (HPEC) 2018
- intro: Carnegie Mellon University
- arxiv: https://arxiv.org/abs/1810.10551
Hybrid Knowledge Routed Modules for Large-scale Object Detection
- intro: NIPS 2018
- arxiv: https://arxiv.org/abs/1810.12681
- github(official, PyTorch): https://github.com/chanyn/HKRM
Gradient Harmonized Single-stage Detector
- intro: AAAI 2019
- arxiv: https://arxiv.org/abs/1811.05181
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
- intro: AAAI 2019
- arxiv: https://arxiv.org/abs/1811.04533
- github: https://github.com/qijiezhao/M2Det
BAN: Focusing on Boundary Context for Object Detection
- arxiv：https://arxiv.org/abs/1811.05243
Multi-layer Pruning Framework for Compressing Single Shot MultiBox Detector
- intro: WACV 2019
- arxiv: https://arxiv.org/abs/1811.08342
R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy
- arxiv: https://arxiv.org/abs/1811.07126
- github: https://github.com/DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow
DeRPN: Taking a further step toward more general object detection
- intro: AAAI 2019
- intro: South China University of Technology
- arxiv: https://arxiv.org/abs/1811.06700
- github: https://github.com/HCIILAB/DeRPN
Fast Efficient Object Detection Using Selective Attention
- arxiv：https://arxiv.org/abs/1811.07502
Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects
- arxiv：https://arxiv.org/abs/1811.10862
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects
- arxiv：https://arxiv.org/abs/1811.12152
Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection
- arxiv：https://arxiv.org/abs/1811.11318
Grid R-CNN
- intro: SenseTime
- arxiv: https://arxiv.org/abs/1811.12030
Transferable Adversarial Attacks for Image and Video Object Detection
-arxiv：https://arxiv.org/abs/1811.12641
Anchor Box Optimization for Object Detection
- intro: University of Illinois at Urbana-Champaign & Microsoft Research
- arxiv: https://arxiv.org/abs/1812.00469
AutoFocus: Efficient Multi-Scale Inference
- intro: University of Maryland
- arxiv: https://arxiv.org/abs/1812.01600
###Few-shot Object Detection via Feature Reweighting
- arxiv：https://arxiv.org/abs/1812.01866
Practical Adversarial Attack Against Object Detector
- arxiv：https://arxiv.org/abs/1812.10217
Learning Efficient Detector with Semi-supervised Adaptive Distillation
- intro: SenseTime Research
- arxiv: https://arxiv.org/abs/1901.00366
- github: https://github.com/Tangshitao/Semi-supervised-Adaptive-Distillation
Scale-Aware Trident Networks for Object Detection
intro: University of Chinese Academy of Sciences & TuSimple
arxiv: https://arxiv.org/abs/1901.01892
github: https://github.com/TuSimple/simpledet
Region Proposal by Guided Anchoring
- intro: CUHK - SenseTime Joint Lab & Amazon Rekognition & Nanyang Technological University
- arxiv: https://arxiv.org/abs/1901.03278
Consistent Optimization for Single-Shot Object Detection
- arxiv: https://arxiv.org/abs/1901.06563
- blog: https://zhuanlan.zhihu.com/p/55416312
Bottom-up Object Detection by Grouping Extreme and Center Points
- keywords: ExtremeNet
- arxiv: https://arxiv.org/abs/1901.08043
- github: https://github.com/xingyizhou/ExtremeNet
A Single-shot Object Detector with Feature Aggragation and Enhancement
- arxiv: https://arxiv.org/abs/1902.02923
Bag of Freebies for Training Object Detection Neural Networks
- intro: Amazon Web Services
- arxiv: https://arxiv.org/abs/1902.04103
Non-Maximum Suppression (NMS)
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
- intro: CVPR 2015
- arxiv: http://arxiv.org/abs/1411.5309
- paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wan_End-to-End_Integration_of_2015_CVPR_paper.pdf
A convnet for non-maximum suppression
- arxiv: http://arxiv.org/abs/1511.06437
Improving Object Detection With One Line of Code
Soft-NMS – Improving Object Detection With One Line of Code
- intro: ICCV 2017. University of Maryland
- keywords: Soft-NMS
- arxiv: https://arxiv.org/abs/1704.04503
- github: https://github.com/bharatsingh430/soft-nms
Learning non-maximum suppression
- intro: CVPR 2017
- project page: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/learning-nms/
- arxiv: https://arxiv.org/abs/1705.02950
- github: https://github.com/hosang/gossipnet
Relation Networks for Object Detection
- intro: CVPR 2018 oral
- arxiv: https://arxiv.org/abs/1711.11575
- github(official, MXNet): https://github.com/msracver/Relation-Networks-for-Object-Detection
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
- keywords: Pairwise-NMS
- arxiv: https://arxiv.org/abs/1901.03796
Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
- arxiv: https://arxiv.org/abs/1902.02067
Adversarial Examples
Adversarial Examples that Fool Detectors
- intro: University of Illinois
- arxiv: https://arxiv.org/abs/1712.02494
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods
- project page: http://nicholas.carlini.com/code/nn_breaking_detection/
- arxiv: https://arxiv.org/abs/1705.07263
- github: https://github.com/carlini/nn_breaking_detection
Weakly Supervised Object Detection
Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection
- intro: CVPR 2016
- arxiv: http://arxiv.org/abs/1604.05766
Weakly supervised object detection using pseudo-strong labels
- arxiv: http://arxiv.org/abs/1607.04731
Saliency Guided End-to-End Learning for Weakly Supervised Object Detection
- intro: IJCAI 2017
- arxiv: https://arxiv.org/abs/1706.06768
Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection
- intro: TPAMI 2017. National Institutes of Health (NIH) Clinical Center
- arxiv: https://arxiv.org/abs/1801.03145
Video Object Detection
Learning Object Class Detectors from Weakly Annotated Video
- intro: CVPR 2012
- paper: https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_00905.pdf
Analysing domain shift factors between videos and images for object detection
- arxiv: https://arxiv.org/abs/1501.01186
Video Object Recognition
- slides: http://vision.princeton.edu/courses/COS598/2015sp/slides/VideoRecog/Video Object Recognition.pptx
Deep Learning for Saliency Prediction in Natural Video
- intro: Submitted on 12 Jan 2016
- keywords: Deep learning, saliency map, optical flow, convolution network, contrast features
- paper: https://hal.archives-ouvertes.fr/hal-01251614/document
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
- intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task
- arxiv: http://arxiv.org/abs/1604.02532
- github: https://github.com/myfavouritekk/T-CNN
Object Detection from Video Tubelets with Convolutional Neural Networks
- intro: CVPR 2016 Spotlight paper
- arxiv: https://arxiv.org/abs/1604.04053
- paper: http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf
- gihtub: https://github.com/myfavouritekk/vdetlib
Object Detection in Videos with Tubelets and Multi-context Cues
- intro: SenseTime Group
- slides: http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf
- slides: http://image-net.org/challenges/talks/Object Detection in Videos with Tubelets and Multi-context Cues - Final.pdf
Context Matters: Refining Object Detection in Video with Recurrent Neural Networks
- intro: BMVC 2016
- keywords: pseudo-labeler
- arxiv: http://arxiv.org/abs/1607.04648
- paper: http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf
CNN Based Object Detection in Large Video Images
- intro: WangTao @ 爱奇艺
- keywords: object retrieval, object detection, scene classification
- slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6362-wang-tao-cnn-based-object-detection-large-video-images.pdf
Object Detection in Videos with Tubelet Proposal Networks
- arxiv: https://arxiv.org/abs/1702.06355
Flow-Guided Feature Aggregation for Video Object Detection
- intro: MSRA
- arxiv: https://arxiv.org/abs/1703.10025
Video Object Detection using Faster R-CNN
- blog: http://andrewliao11.github.io/object_detection/faster_rcnn/
- github: https://github.com/andrewliao11/py-faster-rcnn-imagenet
Improving Context Modeling for Video Object Detection and Tracking
http://image-net.org/challenges/talks_2017/ilsvrc2017_short(poster).pdf
Temporal Dynamic Graph LSTM for Action-driven Video Object Detection
- intro: ICCV 2017
- arxiv: https://arxiv.org/abs/1708.00666
Mobile Video Object Detection with Temporally-Aware Feature Maps
- arxiv: https://arxiv.org/abs/1711.06368
Towards High Performance Video Object Detection
- arxiv: https://arxiv.org/abs/1711.11577
Impression Network for Video Object Detection
- arxiv: https://arxiv.org/abs/1712.05896
Spatial-Temporal Memory Networks for Video Object Detection
- arxiv: https://arxiv.org/abs/1712.06317
3D-DETNet: a Single Stage Video-Based Vehicle Detector
- arxiv: https://arxiv.org/abs/1801.01769
Object Detection in Videos by Short and Long Range Object Linking
- arxiv: https://arxiv.org/abs/1801.09823
Object Detection in Video with Spatiotemporal Sampling Networks
- intro: University of Pennsylvania, 2Dartmouth College
- arxiv: https://arxiv.org/abs/1803.05549
Towards High Performance Video Object Detection for Mobiles
- intro: Microsoft Research Asia
- arxiv: https://arxiv.org/abs/1804.05830
Optimizing Video Object Detection via a Scale-Time Lattice
- intro: CVPR 2018
- project page: http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice/
- arxiv: https://arxiv.org/abs/1804.05472
- github: https://github.com/hellock/scale-time-lattice
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing
- https://arxiv.org/abs/1809.01701
Fast Object Detection in Compressed Video
- arxiv：https://arxiv.org/abs/1811.11057
Tube-CNN: Modeling temporal evolution of appearance for object detection in video
- intro: INRIA/ENS
- arxiv: https://arxiv.org/abs/1812.02619
AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
- intro: SysML 2019 oral
- arxiv: https://arxiv.org/abs/1902.02910