Papers

Restricted Boltzmann Machine (RBM)

Papers

Tutorials

Learning A Deep Compact Image Representation for Visual Tracking

• intro: NIPS 2013
• intro: DLT
• project page: http://winsty.net/dlt.html

Hierarchical Convolutional Features for Visual Tracking

• intro: ICCV 2015
• project page: https://sites.google.com/site/jbhuang0604/publications/cf2
• github: https://github.com/jbhuang0604/CF2

Robust Visual Tracking via Convolutional Networks

• arxiv: http://arxiv.org/abs/1501.04505
• paper: http://kaihuazhang.net/CNT.pdf
• code: http://kaihuazhang.net/CNT_matlab.rar

Transferring Rich Feature Hierarchies for Robust Visual Tracking

• intro: SO-DLT
• arxiv: http://arxiv.org/abs/1501.04587
• slides: http://valse.mmcheng.net/ftp/20150325/RVT.pptx

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

• intro: The Winner of The VOT2015 Challenge
• keywords: Multi-Domain Network (MDNet)
• homepage: http://cvlab.postech.ac.kr/research/mdnet/
• arxiv: http://arxiv.org/abs/1510.07945
• github: https://github.com/HyeonseobNam/MDNet

RATM: Recurrent Attentive Tracking Model

• arxiv: http://arxiv.org/abs/1510.08660
• github: https://github.com/saebrahimi/RATM

Understanding and Diagnosing Visual Tracking Systems

• intro: ICCV 2015
• project page: http://winsty.net/tracker_diagnose.html
• paper: http://winsty.net/papers/diagnose.pdf
• code(Matlab): http://120.52.72.43/winsty.net/c3pr90ntcsf0/diagnose/diagnose_code.zip

Recurrently Target-Attending Tracking

• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Cui_Recurrently_Target-Attending_Tracking_CVPR_2016_paper.html
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Cui_Recurrently_Target-Attending_Tracking_CVPR_2016_paper.pdf

Visual Tracking with Fully Convolutional Networks

• intro: ICCV 2015
• paper: http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf
• github: https://github.com/scott89/FCNT

Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks

• intro: AAAI 2016
• arxiv: http://arxiv.org/abs/1602.00991
• github: https://github.com/pondruska/DeepTracking

Learning to Track at 100 FPS with Deep Regression Networks

• intro: ECCV 2015
• intro: GOTURN: Generic Object Tracking Using Regression Networks
• project page: http://davheld.github.io/GOTURN/GOTURN.html
• arxiv: http://arxiv.org/abs/1604.01802
• github: https://github.com/davheld/GOTURN

Learning by tracking: Siamese CNN for robust target association

• arxiv: http://arxiv.org/abs/1604.07866

Fully-Convolutional Siamese Networks for Object Tracking

• intro: ECCV 2016
• intro: State-of-the-art performance in arbitrary object tracking at 50-100 FPS with Fully Convolutional Siamese networks
• project page: http://www.robots.ox.ac.uk/~luca/siamese-fc.html
• arxiv: http://arxiv.org/abs/1606.09549
• github(official): https://github.com/bertinetto/siamese-fc
• github(official): https://github.com/torrvision/siamfc-tf
• valse-video: http://www.iqiyi.com/w_19ruirwrel.html#vfrm=8-8-0-1

Hedged Deep Tracking

• project page(paper+code): https://sites.google.com/site/yuankiqi/hdt
• paper: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx5dWFua2lxaXxneDoxZjc2MmYwZGIzNjFhYTRl

Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking

• intro: ROLO is short for Recurrent YOLO, aimed at simultaneous object detection and tracking
• project page: http://guanghan.info/projects/ROLO/
• arxiv: http://arxiv.org/abs/1607.05781
• github: https://github.com/Guanghan/ROLO

Visual Tracking via Shallow and Deep Collaborative Model

• arxiv: http://arxiv.org/abs/1607.08040

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

• intro: ECCV 2016
• intro: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate)
• keywords: Continuous Convolution Operator Tracker (C-COT)
• project page: http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html
• arxiv: http://arxiv.org/abs/1608.03773
• github(MATLAB): https://github.com/martin-danelljan/Continuous-ConvOp

Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network

• keywords: Predictive Vision Model (PVM)
• arxiv: http://arxiv.org/abs/1607.06854
• github: https://github.com/braincorp/PVM

Modeling and Propagating CNNs in a Tree Structure for Visual Tracking

• arxiv: http://arxiv.org/abs/1608.07242

Robust Scale Adaptive Kernel Correlation Filter Tracker With Hierarchical Convolutional Features

• paper: http://ieeexplore.ieee.org/document/7496863/

Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks

• arxiv: https://arxiv.org/abs/1609.09365

OTB Results: visual tracker benchmark results

• github: https://github.com/foolwood/benchmark_results

Convolutional Regression for Visual Tracking

• arxiv: https://arxiv.org/abs/1611.04215

Semantic tracking: Single-target tracking with inter-supervised convolutional networks

• arxiv: https://arxiv.org/abs/1611.06395

SANet: Structure-Aware Network for Visual Tracking

• arxiv: https://arxiv.org/abs/1611.06878

ECO: Efficient Convolution Operators for Tracking

• intro: CVPR 2017
• project page: http://www.cvl.isy.liu.se/research/objrec/visualtracking/ecotrack/index.html
• arxiv: https://arxiv.org/abs/1611.09224
• github: https://github.com/martin-danelljan/ECO

Dual Deep Network for Visual Tracking

• arxiv: https://arxiv.org/abs/1612.06053

Deep Motion Features for Visual Tracking

• intro: ICPR 2016. Best paper award in the “Computer Vision and Robot Vision” track
• arxiv: https://arxiv.org/abs/1612.06615

Globally Optimal Object Tracking with Fully Convolutional Networks

• arxiv: https://arxiv.org/abs/1612.08274

Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation

• arxiv: https://arxiv.org/abs/1701.00561
• bitbucket: https://bitbucket.org/xinke_wang/msdat

Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies

• arxiv: https://arxiv.org/abs/1701.01909

Large Margin Object Tracking with Circulant Feature Maps

• intro: CVPR 2017
• intro: The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark sequences while runs at speed in excess of 80 frames per secon
• arxiv: https://arxiv.org/abs/1703.05020
• notes: https://zhuanlan.zhihu.com/p/25761718

DCFNet: Discriminant Correlation Filters Network for Visual Tracking

• arxiv: https://arxiv.org/abs/1704.04057
• github: https://github.com/foolwood/DCFNet

End-to-end representation learning for Correlation Filter based tracking

• intro: CVPR 2017. University of Oxford
• intro: Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to achieve state-of-the-art performance in tracking, at high-speed.
• project page: http://www.robots.ox.ac.uk/~luca/cfnet.html
• arxiv: https://arxiv.org/abs/1704.06036
• gtihub: https://github.com/bertinetto/cfnet

Context-Aware Correlation Filter Tracking

• intro: CVPR 2017 Oral
• project page: https://ivul.kaust.edu.sa/Pages/pub-ca-cf-tracking.aspx
• paper: https://ivul.kaust.edu.sa/Documents/Publications/2017/Context-Aware%20Correlation%20Filter%20Tracking.pdf
• github: https://github.com/thias15/Context-Aware-CF-Tracking

Robust Multi-view Pedestrian Tracking Using Neural Networks

https://arxiv.org/abs/1704.06370

Re3 : Real-Time Recurrent Regression Networks for Object Tracking

• intro: University of Washington
• arxiv: https://arxiv.org/abs/1705.06368
• demo: https://www.youtube.com/watch?v=PC0txGaYz2I

Robust Tracking Using Region Proposal Networks

https://arxiv.org/abs/1705.10447

Hierarchical Attentive Recurrent Tracking

• intro: NIPS 2017. University of Oxford
• arxiv: https://arxiv.org/abs/1706.09262
• github: https://github.com/akosiorek/hart
• results: https://youtu.be/Vvkjm0FRGSs

Siamese Learning Visual Tracking: A Survey

https://arxiv.org/abs/1707.00569

Robust Visual Tracking via Hierarchical Convolutional Features

• project page: https://sites.google.com/site/chaoma99/hcft-tracking
• arxiv: https://arxiv.org/abs/1707.03816
• github: https://github.com/chaoma99/HCFTstar

CREST: Convolutional Residual Learning for Visual Tracking

• intro: ICCV 2017
• project page: http://www.cs.cityu.edu.hk/~yibisong/iccv17/index.html
• arxiv: https://arxiv.org/abs/1708.00225
• github: https://github.com/ybsong00/CREST-Release

Learning Policies for Adaptive Tracking with Deep Feature Cascades

• intro: ICCV 2017 Spotlight
• arxiv: https://arxiv.org/abs/1708.02973

Recurrent Filter Learning for Visual Tracking

• intro: ICCV 2017 Workshop on VOT
• arxiv: https://arxiv.org/abs/1708.03874

Correlation Filters with Weighted Convolution Responses

• intro: ICCV 2017 workshop. 5th visual object tracking(VOT) tracker CFWCR
• paper: http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w28/He_Correlation_Filters_With_ICCV_2017_paper.pdf
• github: https://github.com/he010103/CFWCR

Semantic Texture for Robust Dense Tracking

https://arxiv.org/abs/1708.08844

Learning Multi-frame Visual Representation for Joint Detection and Tracking of Small Objects

Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects

https://arxiv.org/abs/1709.04666

Tracking Persons-of-Interest via Unsupervised Representation Adaptation

• intro: Northwestern Polytechnical University & Virginia Tech & Hanyang University
• keywords: Multi-face tracking
• project page: http://vllab1.ucmerced.edu/~szhang/FaceTracking/
• arxiv: https://arxiv.org/abs/1710.02139

End-to-end Flow Correlation Tracking with Spatial-temporal Attention

https://arxiv.org/abs/1711.01124

UCT: Learning Unified Convolutional Networks for Real-time Visual Tracking

• intro: ICCV 2017 Workshops
• arxiv: https://arxiv.org/abs/1711.04661

Pixel-wise object tracking

https://arxiv.org/abs/1711.07377

MAVOT: Memory-Augmented Video Object Tracking

https://arxiv.org/abs/1711.09414

Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks

https://arxiv.org/abs/1801.02021

Parallel Tracking and Verifying

https://arxiv.org/abs/1801.10496

Saliency-Enhanced Robust Visual Tracking

https://arxiv.org/abs/1802.02783

A Twofold Siamese Network for Real-Time Object Tracking

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1802.08817

Learning Dynamic Memory Networks for Object Tracking

https://arxiv.org/abs/1803.07268

Context-aware Deep Feature Compression for High-speed Visual Tracking

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1803.10537

VITAL: VIsual Tracking via Adversarial Learning

• intro: CVPR 2018 Spotlight
• arixv: https://arxiv.org/abs/1804.04273

Unveiling the Power of Deep Tracking

https://arxiv.org/abs/1804.06833

A Novel Low-cost FPGA-based Real-time Object Tracking System

• intro: ASICON 2017
• arxiv: https://arxiv.org/abs/1804.05535

MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection

https://arxiv.org/abs/1805.00107

Information-Maximizing Sampling to Promote Tracking-by-Detection

https://arxiv.org/abs/1806.02523

Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks

• intro: MICCAI 2018
• arxiv: https://arxiv.org/abs/1806.02070

Stochastic Channel Decorrelation Network and Its Application to Visual Tracking

https://arxiv.org/abs/1807.01103

Fast Dynamic Convolutional Neural Networks for Visual Tracking

https://arxiv.org/abs/1807.03132

DeepTAM: Deep Tracking and Mapping

https://arxiv.org/abs/1808.01900

Distractor-aware Siamese Networks for Visual Object Tracking

• intro: ECCV 2018
• keywords: DaSiamRPN
• arxiv: https://arxiv.org/abs/1808.06048
• github: https://github.com/foolwood/DaSiamRPN

Multi-Branch Siamese Networks with Online Selection for Object Tracking

• intro: ISVC 2018 oral
• arxiv: https://arxiv.org/abs/1808.07349

Real-Time MDNet

• intro: ECCV 2018
• arxiv: https://arxiv.org/abs/1808.08834

Towards a Better Match in Siamese Network Based Visual Object Tracker

• intro: ECCV Visual Object Tracking Challenge Workshop VOT2018
• arxiv: https://arxiv.org/abs/1809.01368

DensSiam: End-to-End Densely-Siamese Network with Self-Attention Model for Object Tracking

• intro: ISVC 2018
• arxiv: https://arxiv.org/abs/1809.02714

Deformable Object Tracking with Gated Fusion

https://arxiv.org/abs/1809.10417

Deep Attentive Tracking via Reciprocative Learning

• intro: NIPS 2018
• project page: https://ybsong00.github.io/nips18_tracking/index
• arxiv: https://arxiv.org/abs/1810.03851
• github: https://github.com/shipubupt/NIPS2018

Online Visual Robot Tracking and Identification using Deep LSTM Networks

• intro: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Award
• arxiv: https://arxiv.org/abs/1810.04941

Detect or Track: Towards Cost-Effective Video Object Detection/Tracking

• intro: AAAI 2019
• arxiv: https://arxiv.org/abs/1811.05340

Deep Siamese Networks with Bayesian non-Parametrics for Video Object Tracking

https://arxiv.org/abs/1811.07386

Fast Online Object Tracking and Segmentation: A Unifying Approach

• intro: CVPR 2019
• preject page: http://www.robots.ox.ac.uk/~qwang/SiamMask/
• arxiv: https://arxiv.org/abs/1812.05050
• github: https://github.com/foolwood/SiamMask

Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking

• intro: Temple University
• arxiv: https://arxiv.org/abs/1812.06148

Handcrafted and Deep Trackers: A Review of Recent Object Tracking Approaches

https://arxiv.org/abs/1812.07368

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

https://arxiv.org/abs/1812.11703

Deeper and Wider Siamese Networks for Real-Time Visual Tracking

https://arxiv.org/abs/1901.01660

SiamVGG: Visual Tracking using Deeper Siamese Networks

https://arxiv.org/abs/1902.02804

TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis

https://arxiv.org/abs/1902.01466

Target-Aware Deep Tracking

• intro: CVPR 2019
• intro: 1Harbin Institute of Technology & Shanghai Jiao Tong University & Tencent AI Lab & University of California & Google Cloud AI
• arxiv: https://arxiv.org/abs/1904.01772

Unsupervised Deep Tracking

• intro: CVPR 2019
• intro: USTC & Tencent AI Lab & Shanghai Jiao Tong University
• arxiv: https://arxiv.org/abs/1904.01828
• github: https://github.com/594422814/UDT
• github: https://github.com/594422814/UDT_pytorch

Generic Multiview Visual Tracking

https://arxiv.org/abs/1904.02553

SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1904.04452

A Strong Feature Representation for Siamese Network Tracker

https://arxiv.org/abs/1907.07880

Visual Tracking via Dynamic Memory Networks

• intro: TPAMI 2019
• arxiv: https://arxiv.org/abs/1907.07613

Multi-Adapter RGBT Tracking

• arxiv: https://arxiv.org/abs/1907.07485
• github: https://github.com/Alexadlu/MANet

Teacher-Students Knowledge Distillation for Siamese Trackers

https://arxiv.org/abs/1907.10586

Tell Me What to Track

• intro: Boston University & Horizon Robotics & University of Chinese Academy of Sciences
• arxiv: https://arxiv.org/abs/1907.11751

Learning to Track Any Object

• intro: ICCV 2019 Holistic Video Understanding workshop
• arxiv: https://arxiv.org/abs/1910.11844

ROI Pooled Correlation Filters for Visual Tracking

• intro: CVPR 2019
• arxiv: https://arxiv.org/abs/1911.01668

D3S – A Discriminative Single Shot Segmentation Tracker

• intro: CVPR 2020
• arxiv: https://arxiv.org/abs/1911.08862
• github(PyTorch): https://github.com/alanlukezic/d3s

Visual Tracking by TridentAlign and Context Embedding

• arxiv: https://arxiv.org/abs/2007.06887
• github: https://github.com/JanghoonChoi/TACT

Transformer Tracking

• intro: CVPR 2021
• intro: Dalian University of Technology & Peng Cheng Laboratory & Remark AI
• arxiv: https://arxiv.org/abs/2103.15436
• github: https://github.com/chenxin-dlut/TransT

Face Tracking

Mobile Face Tracking: A Survey and Benchmark

https://arxiv.org/abs/1805.09749

Multi-Object Tracking (MOT)

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

• arxiv: http://arxiv.org/abs/1605.06457
• dataset(Virtual KITTI): http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds

Multi-Class Multi-Object Tracking using Changing Point Detection

• intro: changing point detection, entity transition, object detection from video, convolutional neural network
• arxiv: http://arxiv.org/abs/1608.08434

POI: Multiple Object Tracking with High Performance Detection and Appearance Feature

• intro: ECCV workshop BMTT 2016. Sensetime
• keywords: KDNT
• arxiv: https://arxiv.org/abs/1610.06136

Simple Online and Realtime Tracking with a Deep Association Metric

• intro: ICIP 2017
• arxiv: https://arxiv.org/abs/1703.07402
• mot challenge: https://motchallenge.net/tracker/DeepSORT_2
• github(official, Python): https://github.com/nwojke/deep_sort
• github(C++): https://github.com/oylz/ds

Multiple Object Tracking: A Literature Review

• intro: last revised 22 May 2017 (this version, v4)
• arxiv: https://arxiv.org/abs/1409.7618

Deep Network Flow for Multi-Object Tracking

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1706.08482

Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism

https://arxiv.org/abs/1708.02843

Recurrent Autoregressive Networks for Online Multi-Object Tracking

https://arxiv.org/abs/1711.02741

SOT for MOT

• intro: Tsinghua University & Megvii Inc. (Face++)
• arxiv: https://arxiv.org/abs/1712.01059

Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project

https://arxiv.org/abs/1712.09531

Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly

https://arxiv.org/abs/1802.03252

Tracking Noisy Targets: A Review of Recent Object Tracking Approaches

https://arxiv.org/abs/1802.03098

Machine Learning Methods for Solving Assignment Problems in Multi-Target Tracking

• intro: University of Florida
• arxiv: https://arxiv.org/abs/1802.06897

Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World

• intro: University of Modena and Reggio Emilia
• arxiv: https://arxiv.org/abs/1803.08319

Features for Multi-Target Multi-Camera Tracking and Re-Identification

• intro: CVPR 2018 spotlight
• intro: https://arxiv.org/abs/1803.10859

High Performance Visual Tracking with Siamese Region Proposal Network

• intro: CVPR 2018 spotlight
• keywords: SiamRPN
• paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_High_Performance_Visual_CVPR_2018_paper.pdf
• slides: https://drive.google.com/file/d/1OGIOUqANvYfZjRoQfpiDqhPQtOvPCpdq/view

Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking

• intro: Peking University
• arxiv: https://arxiv.org/abs/1804.04555

Automatic Adaptation of Person Association for Multiview Tracking in Group Activities

• intro: Carnegie Mellon University & Argo AI & Adobe Research
• project page: http://www.cs.cmu.edu/~ILIM/projects/IM/Association4Tracking/
• arxiv: https://arxiv.org/abs/1805.08717

Improving Online Multiple Object tracking with Deep Metric Learning

https://arxiv.org/abs/1806.07592

Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking

• intro: Tsinghua Univeristy & Horizon Robotics
• arxiv: https://arxiv.org/abs/1808.01562

Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector

• intro: 13th International Symposium on Visual Computing (ISVC)
• arxiv: https://arxiv.org/abs/1809.02073

Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

https://arxiv.org/abs/1809.03137

Deep Affinity Network for Multiple Object Tracking

• intro: IEEE TPAMI 2018
• arxiv: https://arxiv.org/abs/1810.11780
• github: https://github.com/shijieS/SST

Exploit the Connectivity: Multi-Object Tracking with TrackletNet

https://arxiv.org/abs/1811.07258

Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification

• intro: Sensetime Group Limited & Beihang University & The University of Sydney
• arxiv: https://arxiv.org/abs/1901.06129

Online Multi-Object Tracking with Dual Matching Attention Networks

• intro: ECCV 2018
• arxiv: https://arxiv.org/abs/1902.00749

Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment

https://arxiv.org/abs/1902.08231

Tracking without bells and whistles

• intro: Technical University of Munich
• keywords: Tracktor
• arxiv: https://arxiv.org/abs/1903.05625
• github: https://github.com/phil-bergmann/tracking_wo_bnw

Spatial-Temporal Relation Networks for Multi-Object Tracking

• intro: Hong Kong University of Science and Technology & Tsinghua University & MSRA
• arxiv: https://arxiv.org/abs/1904.11489

Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking

• intro: Baidu X-Lab & UC Irvine
• arxiv: https://arxiv.org/abs/1905.11026

State-aware Re-identification Feature for Multi-target Multi-camera Tracking

• intro: CVPR-2019 TRMTMCT Workshop
• intro: BUPT & Chinese Academy of Sciences & Horizon Robotics
• arxiv: https://arxiv.org/abs/1906.01357

DeepMOT: A Differentiable Framework for Training Multiple Object Trackers

• intro: Inria
• keywords: deep Hungarian network (DHN)
• arxiv: https://arxiv.org/abs/1906.06618
• gitlab: https://gitlab.inria.fr/yixu/deepmot

Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking

• intro: Beihang University && Inception Institute of Artificial Intelligence
• arxiv: https://arxiv.org/abs/1907.05315

End-to-End Learning Deep CRF models for Multi-Object Tracking

https://arxiv.org/abs/1907.12176

End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning

• intro: University of Oxford
• arxiv: https://arxiv.org/abs/1907.12887

Robust Multi-Modality Multi-Object Tracking

• intro: ICCV 2019
• keywords: LiDAR
• arxiv: https://arxiv.org/abs/1909.03850
• github: https://github.com/ZwwWayne/mmMOT

Learning Multi-Object Tracking and Segmentation from Automatic Annotations

https://arxiv.org/abs/1912.02096

Learning a Neural Solver for Multiple Object Tracking

• intro: Technical University of Munich
• keywords: Message Passing Networks (MPNs)
• arxiv: https://arxiv.org/abs/1912.07515
• github: https://github.com/dvl-tum/mot_neural_solver

Multi-object Tracking via End-to-end Tracklet Searching and Ranking

• intro: Horizon Robotics Inc
• arxiv: https://arxiv.org/abs/2003.02795

Refinements in Motion and Appearance for Online Multi-Object Tracking

https://arxiv.org/abs/2003.07177

A Unified Object Motion and Affinity Model for Online Multi-Object Tracking

• intro: CVPR 2020
• arxiv: https://arxiv.org/abs/2003.11291
• github: https://github.com/yinjunbo/UMA-MOT

A Simple Baseline for Multi-Object Tracking

• intro: Microsoft Research Asia
• arxiv: https://arxiv.org/abs/2004.01888
• github: https://github.com/ifzhang/FairMOT

MOPT: Multi-Object Panoptic Tracking

• intro: University of Freiburg
• arxiv: https://arxiv.org/abs/2004.08189

SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking

• intro: Tsinghua University & Megvii Inc
• arxiv: https://arxiv.org/abs/2004.07472

Multi-Object Tracking with Siamese Track-RCNN

• intro: Amazon Web Service (AWS) Rekognition
• arixv: https://arxiv.org/abs/2004.07786

TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model

• intro: CVPR 2020 oral
• arxiv: https://arxiv.org/abs/2006.05683
• github: https://github.com/BoPang1996/TubeTK

Quasi-Dense Similarity Learning for Multiple Object Tracking

• intro: CVPR 2021 oral
• arxiv: https://arxiv.org/abs/2006.06664
• github: https://github.com/SysCV/qdtrack

imultaneous Detection and Tracking with Motion Modelling for Multiple Object Tracking

• intro: ECCV 2020
• arxiv: https://arxiv.org/abs/2008.08826
• github: https://github.com/shijieS/DMMN

MAT: Motion-Aware Multi-Object Tracking

https://arxiv.org/abs/2009.04794

SAMOT: Switcher-Aware Multi-Object Tracking and Still Another MOT Measure

https://arxiv.org/abs/2009.10338

GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization

• intro: Virginia Tech
• arxiv: https://arxiv.org/abs/2010.00067

Rethinking the competition between detection and ReID in Multi-Object Tracking

• intro: University of Electronic Science and Technology of China(UESTC) & Chinese Academy of Sciences
• arxiv: https://arxiv.org/abs/2010.12138

GMOT-40: A Benchmark for Generic Multiple Object Tracking

• intro: Temple University & Stony Brook University & Microsoft
• arxiv: https://arxiv.org/abs/2011.11858

Multi-object Tracking with a Hierarchical Single-branch Network

https://arxiv.org/abs/2101.01984

Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking

• intro: Georgia Institute of Technology & Oregon State University
• arxiv: https://arxiv.org/abs/2101.12159

Learning a Proposal Classifier for Multiple Object Tracking

• intro: CVPR 2021 poster
• arxiv: https://arxiv.org/abs/2103.07889
• github: https://github.com/daip13/LPC_MOT

Track to Detect and Segment: An Online Multi-Object Tracker

• intro: CVPR 2021
• intro: SUNY Buffalo & TJU & Horizon Robotics
• project page: https://jialianwu.com/projects/TraDeS.html
• arxiv: https://arxiv.org/abs/2103.08808

Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking

• intro: CVPR 2021
• arxiv: https://arxiv.org/abs/2103.16178
• github: https://github.com/jiaweihe1996/GMTracker

Multiple Object Tracking with Correlation Learning

• intro: CVPR 2021
• arxiv: https://arxiv.org/abs/2104.03541

Transformer

TransTrack: Multiple-Object Tracking with Transformer

• intro: The University of Hong Kong & ByteDance AI Lab & Tongji University & Carnegie Mellon University & Nanyang Technological University
• arxiv: https://arxiv.org/abs/2012.15460
• github: https://github.com/PeizeSun/TransTrack

TrackFormer: Multi-Object Tracking with Transformers

• intro: Technical University of Munich & Facebook AI Research (FAIR)
• arxiv: https://arxiv.org/abs/2101.02702

TransCenter: Transformers with Dense Queries for Multiple-Object Tracking

• intro: Inria & MIT & MIT-IBM Watson AI Lab
• arxiv: https://arxiv.org/abs/2103.15145

Looking Beyond Two Frames: End-to-End Multi-Object Tracking UsingSpatial and Temporal Transformers

• intro: Monash University & The University of Adelaide & Australian Centre for Robotic Vision
• arixiv: https://arxiv.org/abs/2103.14829

TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking

• intro: Microsoft & Stony Brook University
• arxiv: https://arxiv.org/abs/2104.00194

Multiple People Tracking

Multi-Person Tracking by Multicut and Deep Matching

• intro: Max Planck Institute for Informatics
• arxiv: http://arxiv.org/abs/1608.05404

Joint Flow: Temporal Flow Fields for Multi Person Tracking

• intro: University of Bonn
• arxiv: https://arxiv.org/abs/1805.04596

Multiple People Tracking by Lifted Multicut and Person Re-identification

• intro: CVPR 2017
• intro: Max Planck Institute for Informatics & Max Planck Institute for Intelligent Systems
• paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Tang_Multiple_People_Tracking_CVPR_2017_paper.pdf
• code: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/people-detection-pose-estimation-and-tracking/multiple-people-tracking-with-lifted-multicut-and-person-re-identification/

Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking

• intro: WACV 2018
• intro: Queensland University of Technology (QUT)
• arxiv: https://arxiv.org/abs/1803.03347

Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification

• intro: ICME 2018
• arxiv: https://arxiv.org/abs/1809.04427
• github: https://github.com/longcw/MOTDT

Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking

https://arxiv.org/abs/1810.08565

Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification

https://arxiv.org/abs/1811.04091

Multi-person Articulated Tracking with Spatial and Temporal Embeddings

• intro: CVPR 2019
• intro: SenseTime Research & The University of Sydney & SenseTime Computer Vision Research Group
• arxiv: https://arxiv.org/abs/1903.09214

Instance-Aware Representation Learning and Association for Online Multi-Person Tracking

• intro: Pattern Recognition
• intro: Sun Yat-sen University & Guangdong University of Foreign Studies & Carnegie Mellon University & University of California & Guilin University of Electronic Technology & WINNER Technology
• arxiv: https://arxiv.org/abs/1905.12409

Online Multiple Pedestrian Tracking using Deep Temporal Appearance Matching Association

• intro: 2nd ranked tracker of the MOTChallenge on CVPR19 workshop
• arxiv: https://arxiv.org/abs/1907.00831

Detecting Invisible People

• intro: Carnegie Mellon University & Argo AI
• project page: http://www.cs.cmu.edu/~tkhurana/invisible.htm
• arxiv: https://arxiv.org/abs/2012.08419

MOTS

MOTS: Multi-Object Tracking and Segmentation

• intro: CVPR 2019
• intro: RWTH Aachen University
• keywords: TrackR-CNN
• project page: https://www.vision.rwth-aachen.de/page/mots
• arxiv: https://arxiv.org/abs/1902.03604
• github(official): https://github.com/VisualComputingInstitute/TrackR-CNN

Segment as Points for Efficient Online Multi-Object Tracking and Segmentation

• intro: ECCV 2020 oral
• intro: PointTrack
• arxiv: https://arxiv.org/abs/2007.01550
• github: https://github.com/detectRecog/PointTrack

PointTrack++ for Effective Online Multi-Object Tracking and Segmentation

• intro: CVPR2020 MOTS Challenge Winner. PointTrack++ ranks first on KITTI MOTS
• arxiv: https://arxiv.org/abs/2007.01549

Multi-target multi-camera tracking (MTMCT)

Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model

• intro: ACMMM 2020
• arxiv: https://arxiv.org/abs/2008.09785

3D MOT

A Baseline for 3D Multi-Object Tracking

• arxiv: https://arxiv.org/abs/1907.03961
• github: https://github.com/xinshuoweng/AB3DMOT

Probabilistic 3D Multi-Object Tracking for Autonomous Driving

• intro: NeurIPS 2019
• intro: 1st Place Award, NuScenes Tracking Challenge
• intro: Stanford University $ Toyota Research Institute
• arxiv: https://arxiv.org/abs/2001.05673
• github: https://github.com/eddyhkchiu/mahalanobis_3d_multi_object_tracking

JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset

• intro: Stanford University
• arxiv: https://arxiv.org/abs/2002.08397
• github: https://github.com/StanfordVL/JRMOT_ROS

Real-time 3D Deep Multi-Camera Tracking

• intro: Microsoft Cloud & AI
• arxiv: https://arxiv.org/abs/2003.11753

P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds

• intro: CVPR 2020 oral
• intro: Huazhong University of Science and Technology
• arxiv: https://arxiv.org/abs/2005.13888
• github: https://github.com/HaozheQi/P2B

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

• intro: CVPR 2020
• intro: Uber Advanced Technologies Group & University of Toronto
• arxiv: https://arxiv.org/abs/2005.14711

GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning

• intro: CVPR 2020
• intro: Carnegie Mellon University
• arxiv: https://arxiv.org/abs/2006.07327
• github(official, PyTorch): https://github.com/xinshuoweng/GNN3DMOT

1st Place Solutions for Waymo Open Dataset Challenges – 2D and 3D Tracking

• intro: Horizon Robotics Inc.
• arxiv: https://arxiv.org/abs/2006.15506

Graph Neural Networks for 3D Multi-Object Tracking

• intro: ECCV 2020 workshop
• intro: Robotics Institute, Carnegie Mellon University
• project page: http://www.xinshuoweng.com/projects/GNN3DMOT/
• arxiv: https://arxiv.org/abs/2008.09506
• github: https://github.com/xinshuoweng/GNN3DMOT

Learnable Online Graph Representations for 3D Multi-Object Tracking

https://arxiv.org/abs/2104.11747

Single Stage Joint Detection and Tracking

Bridging the Gap Between Detection and Tracking: A Unified Approach

• intro: ICCV 2019
• paper: https://openaccess.thecvf.com/content_ICCV_2019/papers/Huang_Bridging_the_Gap_Between_Detection_and_Tracking_A_Unified_Approach_ICCV_2019_paper.pdf

Towards Real-Time Multi-Object Tracking

• intro: Tsinghua University & Austrilian National University
• arxiv: https://arxiv.org/abs/1909.12605
• github: https://github.com/Zhongdao/Towards-Realtime-MOT

RetinaTrack: Online Single Stage Joint Detection and Tracking

• intro: CVPR 2020
• intro: Google
• arxiv: https://arxiv.org/abs/2003.13870

Tracking Objects as Points

• intro: UT Austin & Intel Labs
• intro: Simultaneous object detection and tracking using center points.
• keywords: CenterTrack
• arxiv: https://arxiv.org/abs/2004.01177
• github: https://github.com/xingyizhou/CenterTrack

Fully Convolutional Online Tracking

• intro: Nanjing University
• arxiv: https://arxiv.org/abs/2004.07109
• github(official, PyTorch): https://github.com/MCG-NJU/FCOT

Accurate Anchor Free Tracking

• intro: Tongji University & UCLA
• keywords: Anchor Free Siamese Network (AFSN)
• arxiv: https://arxiv.org/abs/2006.07560

Ocean: Object-aware Anchor-free Tracking

• intro: ECCV 2020
• intro: NLPR, CASIA & UCAS & Microsoft Research
• arxiv: https://arxiv.org/abs/2006.10721
• github: https://github.com/researchmm/TracKit

Joint Detection and Multi-Object Tracking with Graph Neural Networks

• intro: Carnegie Mellon University
• arxiv: https://arxiv.org/abs/2006.13164

Joint Multiple-Object Detection and Tracking

Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking

• intro: ECCV 2020 spotlight
• intro: Tencent Youtu Lab & Fudan University & Nara Institute of Science and Technology
• keywords: Chained-Tracker (CTracker)
• arxiv: https://arxiv.org/abs/2007.14557
• github: https://github.com/pjl1995/CTracker

SMOT: Single-Shot Multi Object Tracking

https://arxiv.org/abs/2010.16031

DEFT: Detection Embeddings for Tracking

• arxiv: https://arxiv.org/abs/2102.02267
• github: https://github.com/MedChaabane/DEFT

Global Correlation Network: End-to-End Joint Multi-Object Detection and Tracking

• intro: ICCV 2021
• intro: Intell Tsinghua University & Beihang University
• arxiv: https://arxiv.org/abs/2103.12511

Tracking with Reinforcement Learning

Deep Reinforcement Learning for Visual Object Tracking in Videos

• intro: University of California at Santa Barbara & Samsung Research America
• arxiv: https://arxiv.org/abs/1701.08936

Visual Tracking by Reinforced Decision Making

• arxiv: https://arxiv.org/abs/1702.06291

End-to-end Active Object Tracking via Reinforcement Learning

https://arxiv.org/abs/1705.10561

Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning

• project page: https://sites.google.com/view/cvpr2017-adnet
• paper: https://drive.google.com/file/d/0B34VXh5mZ22cZUs2Umc1cjlBMFU/view?usp=drive_web

Tracking as Online Decision-Making: Learning a Policy from Streaming Videos with Reinforcement Learning

https://arxiv.org/abs/1707.04991

Detect to Track and Track to Detect

• intro: ICCV 2017
• project page: https://www.robots.ox.ac.uk/~vgg/research/detect-track/
• arxiv: https://arxiv.org/abs/1710.03958
• github: https://github.com/feichtenhofer/Detect-Track

Projects

MMTracking

• intro: OpenMMLab Video Perception Toolbox. It supports Single Object Tracking (SOT), Multiple Object Tracking (MOT), Video Object Detection (VID) with a unified framework.
• github: https://github.com/open-mmlab/mmtracking

Tensorflow_Object_Tracking_Video

• intro: Object Tracking in Tensorflow ( Localization Detection Classification ) developed to partecipate to ImageNET VID competition
• github: https://github.com/DrewNF/Tensorflow_Object_Tracking_Video

Resources

Multi-Object-Tracking-Paper-List

• intro: Paper list and source code for multi-object-tracking
• github: https://github.com/SpyderXu/multi-object-tracking-paper-list

Papers

Neural Art

A Neural Algorithm of Artistic Style

• arxiv: http://arxiv.org/abs/1508.06576
• gitxiv: http://gitxiv.com/posts/jG46ukGod8R7Rdtud/a-neural-algorithm-of-artistic-style
• github: https://github.com/kaishengtai/neuralart
• github: https://github.com/jcjohnson/neural-style
• github: https://github.com/andersbll/neural_artistic_style
• ipn: http://nbviewer.ipython.org/github/Lasagne/Recipes/blob/master/examples/styletransfer/Art%20Style%20Transfer.ipynb
• github: https://github.com/mbartoli/neural-animation
• github: https://github.com/memisevic/artify
• github: https://github.com/mattya/chainer-gogh
• github(TensorFlow): https://github.com/anishathalye/neural-style
• github: https://github.com/woodrush/neural-art-tf
• github: https://github.com/dmlc/mxnet/tree/master/example/neural-style
• demo: http://deepart.io/
• github: https://github.com/Teaonly/easyStyle
• github: https://github.com/ckmarkoh/neuralart_tensorflow
• github: https://github.com/fzliu/style-transfer
• github: https://github.com/titu1994/Neural-Style-Transfer
• github: https://github.com/saikatbsk/Vincent-AI-Artist
• github: https://github.com/zhaw/neural_style

Image Style Transfer Using Convolutional Neural Networks

• paper: www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf

Artificial Startup Style: Neural art about startup fashion

• blog: https://medium.com/data-engineering/artificial-startup-style-437f6090b1f7#.8u06gq42e

From Pixels to Paragraphs: How artistic experiments with deep learning guard us from hype

• blog: https://medium.com/@genekogan/from-pixels-to-paragraphs-eb2763da0e9b#.er3djn9z9

Experiments with style transfer

• website: http://mtyka.github.io/code/2015/10/02/experiments-with-style-transfer.html

Style Transfer for Headshot Portraits (SIGGRAPH 2014)

• project: https://people.csail.mit.edu/yichangshih/portrait_web/

Teaching recurrent Neural Networks about Monet

• blog: http://blog.manugarri.com/teaching-recurrent-neural-networks-about-monet/
• github: https://github.com/manugarri/keras_monet

Content Aware Neural Style Transfer

• arxiv: http://arxiv.org/abs/1601.04568

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

• arxiv: http://arxiv.org/abs/1601.04589
• github: https://github.com/chuanli11/CNNMRF

Stylenet: Neural Network with Style Synthesis

• github: https://github.com/machrisaa/stylenet

Ostagram

• intro: This program presents web-service for algorithm combining the content of one image with the style of another image using convolutional neural networks
• github: https://github.com/SergeyMorugin/ostagram

Exploring the Neural Algorithm of Artistic Style

• intro: A short class project report
• arxiv: http://arxiv.org/abs/1602.07188

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

• intro: Justin Johnson, Alexandre Alahi, Li Fei-Fei. ECCV 2016
• arxiv: http://arxiv.org/abs/1603.08155
• github: https://github.com/jcjohnson/fast-neural-style
• github: https://github.com/yusuketomoto/chainer-fast-neuralstyle
• github: https://github.com/awentzonline/keras-rtst
• github(Keras): https://github.com/titu1994/Fast-Neural-Style
• gtihub(Tensorflow): [https://github.com/junrushao1994/fast-neural-style.tf] (https://github.com/junrushao1994/fast-neural-style.tf)
• github(PyTorch): https://github.com/bengxy/FastNeuralStyle

Image transformation networks with fancy loss functions

• intro: Fast neural style in tensorflow based on http://arxiv.org/abs/1603.08155
• blog: http://olavnymoen.com/2016/07/07/image-transformation-network
• github: https://github.com/OlavHN/fast-neural-style

Improving the Neural Algorithm of Artistic Style

• arxiv: http://arxiv.org/abs/1605.04603

CubistMirror: an openframeworks app which repeatedly applies real-time style transfer on a webcam

• github: https://github.com/genekogan/CubistMirror

Transfer Style But Not Color

• blog: http://blog.deepart.io/2016/06/04/color-independent-style-transfer/
• github: https://github.com/pavelgonchar/color-independent-style-transfer

neural-art-mini: Lightweight version of mxnet neural art implementation

• intro: Lightweight version of mxnet neural art implementation using ~4.8M SqueezeNet model. Compressed model is less than 500KB
• github: https://github.com/pavelgonchar/neural-art-mini

Preserving Color in Neural Artistic Style Transfer

• arxiv: http://arxiv.org/abs/1606.05897

End to End Neural Art with Generative Models

• blog: http://dmlc.ml/mxnet/2016/06/20/end-to-end-neural-style.html
• github: https://github.com/dmlc/mxnet/tree/master/example/neural-style

Neural Style Explained

• blog: http://kvfrans.com/neural-style-explained/
• github: https://github.com/kvfrans/neural-style

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

• intro: IMCL 2016
• arxiv: http://arxiv.org/abs/1603.03417
• github: https://github.com/DmitryUlyanov/texture_nets
• notes: https://blog.acolyer.org/2016/09/23/texture-networks-feed-forward-synthesis-of-textures-and-stylized-images/
• github: https://github.com/tgyg-jegli/tf_texture_net

Learning Typographic Style

• arxiv: https://arxiv.org/abs/1603.04000

Instance Normalization: The Missing Ingredient for Fast Stylization

• arxiv: http://arxiv.org/abs/1607.08022

Painting style transfer for head portraits using convolutional neural networks

• paper: http://dl.acm.org/citation.cfm?id=2925968
• sci-hub: http://dl.acm.org.sci-hub.cc/citation.cfm?doid=2897824.2925968

Style-Transfer via Texture-Synthesis

• arxiv: http://arxiv.org/abs/1609.03057

neural-style-tf: TensorFlow implementation of Neural Style

• github: https://github.com/cysmith/neural-style-tf

Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity

• intro: In Proceedings of the 7th International Conference on Computational Creativity. Palo Alto: Association for the Advancement of Artificial Intelligence (AAAI) Press (2016)
• arxiv: https://arxiv.org/abs/1610.02478

A Learned Representation For Artistic Style

• intro: Google Brain
• arxiv: https://arxiv.org/abs/1610.07629
• blog: https://research.googleblog.com/2016/10/supercharging-style-transfer.html
• github: https://github.com/tensorflow/magenta/tree/master/magenta/models/image_stylization
• github(Tensorflow): https://github.com/Heumi/Fast_Multi_Style_Transfer-tf

How to Fake It As an Artist with Docker, AWS and Deep Learning

• blog: https://medium.com/@lherrera/how-to-fake-it-as-an-artist-with-docker-aws-and-deep-learning-6d42f4acd890#.eq9gcgkzh

Multistyle Pastiche Generator

• blog: https://magenta.tensorflow.org/2016/11/01/multistyle-pastiche-generator/

Fast Style Transfer in TensorFlow

• intro: Video Stylization, Image Stylization
• github: https://github.com/lengstrom/fast-style-transfer

Neural Style Transfer For Chinese Fonts

• github: https://github.com/kaonashi-tyc/Rewrite

Neural Style Representations and the Large-Scale Classification of Artistic Style

• arxiv: https://arxiv.org/abs/1611.05368

Controlling Perceptual Factors in Neural Style Transfer

• intro: University of Tubingen & Adobe Research
• arxiv: https://arxiv.org/abs/1611.07865

Awesome Typography: Statistics-Based Text Effects Transfer

• arxiv: https://arxiv.org/abs/1611.09026

Fast Patch-based Style Transfer of Arbitrary Style

• arxiv: https://arxiv.org/abs/1612.04337
• github: https://github.com/rtqichen/style-swap

Demystifying Neural Style Transfer

• intro: IJCAI 2017
• intro: Peking University & TuSimple
• arxiv: https://arxiv.org/abs/1701.01036
• github: https://github.com/lyttonhao/Neural-Style-MMD

Son of Zorn’s Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentation

• arxiv: https://arxiv.org/abs/1701.02357

Bringing Impressionism to Life with Neural Style Transfer in Come Swim

• intro: a case study of how Neural Style Transfer can be used in a movie production context
• keywords: Kristen Stewart !
• arxiv: https://arxiv.org/abs/1701.04928

Pytorch tutorials for Neural Style transfert

• github: https://github.com/alexis-jacq/Pytorch-Tutorials

Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses

• arxiv: https://arxiv.org/abs/1701.08893

Arbitrary Style Transfer In Real-Time With Adaptive Instance Normalization

• intro: ICCV 2017. Cornell University
• paper: https://openreview.net/pdf?id=B1fUVMzKg
• github(Torch): https://github.com/xunhuang1995/AdaIN-style
• github(TensorFlow): https://github.com/elleryqueenhomels/arbitrary_style_transfer

Picking an optimizer for Style Transfer

• blog: https://medium.com/slavv/picking-an-optimizer-for-style-transfer-86e7b8cba84b#.cgv2oreaq
• github: https://github.com/slavivanov/Style-Tranfer

Multi-style Generative Network for Real-time Transfer

https://arxiv.org/abs/1703.06953

Deep Photo Style Transfer

• arxiv: https://arxiv.org/abs/1703.07511
• github(Torch): https://github.com/luanfujun/deep-photo-styletransfer
• github(Docker): https://github.com/martinbenson/deep-photo-styletransfer

Lightweight Neural Style on Pytorch

https://github.com/lizeng614/SqueezeNet-Neural-Style-Pytorch

StyleBank: An Explicit Representation for Neural Image Style Transfer

• intro: CVPR 2017
• arxiv: https://arxiv.org/abs/1703.09210

How to Make an Image More Memorable? A Deep Style Transfer Approach

• intro: ACM ICMR 2017
• arxiv: https://arxiv.org/abs/1704.01745

Visual Attribute Transfer through Deep Image Analogy

• intro: SIGGRAPH 2017
• keywords: Deep Image Analogy
• arxiv: https://arxiv.org/abs/1705.01088
• github: https://github.com/msracver/Deep-Image-Analogy

Characterizing and Improving Stability in Neural Style Transfer

https://arxiv.org/abs/1705.02092

Towards Metamerism via Foveated Style Transfer

https://arxiv.org/abs/1705.10041

Style Transfer for Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN

https://arxiv.org/abs/1706.03319

Meta Networks for Neural Style Transfer

• arxiv: https://arxiv.org/abs/1709.04111
• github: https://github.com/FalongShen/styletransfer

Neural Color Transfer between Images

• intro: Hong Kong University of Science and Technology & Microsoft Research
• arxiv: https://arxiv.org/abs/1710.00756

Improved Style Transfer by Respecting Inter-layer Correlations

https://arxiv.org/abs/1801.01933

Face Destylization

https://arxiv.org/abs/1802.01237

Unsupervised Typography Transfer

https://arxiv.org/abs/1802.02595

Stereoscopic Neural Style Transfer

• intro: CVPR 2018
• arxiv: https://arxiv.org/abs/1802.10591

Arbitrary Style Transfer with Deep Feature Reshuffle

https://arxiv.org/abs/1805.04103

Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration

• intro: CVPR 2018. CUHK & SenseTime
• arxiv: https://arxiv.org/abs/1805.03857

Beyond Textures: Learning from Multi-domain Artistic Images for Arbitrary Style Transfer

https://arxiv.org/abs/1805.09987

A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer

https://arxiv.org/abs/1806.00868

Unbiased Image Style Transfer

https://arxiv.org/abs/1807.01424

Uncorrelated Feature Encoding for Faster Image Style Transfer

https://arxiv.org/abs/1807.01493

Adjustable Real-time Style Transfer

• intro: University of Illinois at Urbana-Champaign && Google Brain
• arxiv: https://arxiv.org/abs/1811.08560

Automated Deep Photo Style Transfer

• intro: University of Tubingen
• arxiv: https://arxiv.org/abs/1901.03915

Attention-aware Multi-stroke Style Transfer

https://arxiv.org/abs/1901.05127

StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks

https://arxiv.org/abs/1906.02470

StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks

https://arxiv.org/abs/1906.02470

Neural Art On Audio

MSc AI Project on generative deep networks and neural style transfer for audio

• github: https://github.com/Fr-d-rik/generative_audio
• project report: https://github.com/Fr-d-rik/generative_audio/blob/master/docs/project_report.pdf

Neural Song Style

• intro: Audio style transfer AI
• github: https://github.com/rupeshs/neuralsongstyle

Time Domain Neural Audio Style Transfer

• intro: NIPS 2017
• arxiv: https://arxiv.org/abs/1711.11160
• github: https://github.com//pkmital/time-domain-neural-audio-style-transfer

Learning Linear Transformations for Fast Arbitrary Style Transfer

https://arxiv.org/abs/1808.04537

Neural Art On Video

neural-style-video

• blog: http://larseidnes.com/2015/12/18/painting-videos-with-neural-networks/
• github: https://github.com/larspars/neural-style-video

Instructions for making a Neural-Style movie

• website: https://gist.github.com/genekogan/d61c8010d470e1dbe15d
• sample: https://vimeo.com/139123754

Artistic style transfer for videos

• arxiv: http://arxiv.org/abs/1604.08610
• github: https://github.com/manuelruder/artistic-videos

Artistic style transfer for videos and spherical images

https://arxiv.org/abs/1708.04538

How Deep Learning Can Paint Videos in the Style of Art’s Great Masters

• blog: https://blogs.nvidia.com/blog/2016/05/25/deep-learning-paints-videos/

DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies

• arxiv: http://arxiv.org/abs/1605.08153

Coherent Online Video Style Transfer

https://arxiv.org/abs/1703.09211

Laplacian-Steered Neural Style Transfer

• intro: ACM Multimedia Conference (MM) 2017
• arxiv: https://arxiv.org/abs/1707.01253

Real-Time Neural Style Transfer for Videos

• intro: Tsinghua University & Tencent AI Lab
• paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Real-Time_Neural_Style_CVPR_2017_paper.pdf

Multi-Content GAN for Few-Shot Font Style Transfer

https://arxiv.org/abs/1712.00516

Deep Painterly Harmonization

• arxiv: https://arxiv.org/abs/1804.03189
• github: https://github.com/luanfujun/deep-painterly-harmonization

ReCoNet: Real-time Coherent Video Style Transfer Network

• intro: The University of Hong Kong
• arixv: https://arxiv.org/abs/1807.01197
• supp: https://www.dropbox.com/s/go6f7uopjjsala7/ReCoNet%20Supplementary%20Material.pdf?dl=0

Fully-Featured Attribute Transfer

https://arxiv.org/abs/1902.06258

Neural Doodle

Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks

• paper: http://nucl.ai/semantic-style-transfer.pdf
• reddit: https://www.reddit.com/r/MachineLearning/comments/48zstj/my_wip_implementation_of_neural_image_analogies/
• github: https://github.com/alexjc/neural-doodle

Neural Doodle

• github: https://github.com/alexjc/neural-doodle

Faster neural doodle

• github: https://github.com/DmitryUlyanov/fast-neural-doodle

Feed-forward neural doodle

• blog: http://dmitryulyanov.github.io/feed-forward-neural-doodle/
• github: https://github.com/DmitryUlyanov/online-neural-doodle
• demo: http://likemo.net/

neural image analogies: Generate image analogies using neural matching and blending

• github: https://github.com/awentzonline/image-analogies

Neural doodle with Keras

https://github.com/fchollet/keras/blob/master/examples/neural_doodle.py

Deep Dreams

deepdream

• github: https://github.com/google/deepdream

cnn-vis: Use CNNs to generate images

• github: https://github.com/jcjohnson/cnn-vis

bat-country: A lightweight, extendible, easy to use Python package for deep dreaming and image generation with Caffe and CNNs

• github: https://github.com/jrosebr1/bat-country

DeepDreaming with TensorFlow

• ipn: http://nbviewer.jupyter.org/github/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb

deepdraw

• github: https://github.com/auduno/deepdraw

Understanding Deep Dreams

• blog: http://www.alanzucconi.com/2015/07/06/live-your-deepdream-how-to-recreate-the-inceptionism-effect/

Generating Deep Dreams

• blog：http://www.alanzucconi.com/2016/05/25/generating-deep-dreams/

Audio Deepdream: Optimizing Raw Audio With Convolutional Networks

• intro: Google Brain
• paper: https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/08/ardila-audio.pdf
• examples: https://drive.google.com/drive/folders/0B7NUtSSG_AgqZWF1ajdhSjhEMlk

Image Stylization

Automatic Portrait Segmentation for Image Stylization

• intro: The Chinese University of Hong Kong & Adobe Research
• project page(data+code): http://xiaoyongshen.me/webpage_portrait/index.html
• paper: http://www.cse.cuhk.edu.hk/leojia/papers/portrait_eg16.pdf
• github(Tensorflow): https://github.com/PetroWu/AutoPortraitMatting

Transfiguring Portraits

• paper: http://homes.cs.washington.edu/~kemelmi/Transfiguring_Portraits_Kemelmacher_SIGGRAPH2016.pdf

Stylize Aesthetic QR Code

• intro: Zhengzhou University & Zhejiang University
• arxiv: https://arxiv.org/abs/1803.01146

Papers