Papers

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

• auothor: Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell
• arxiv: http://arxiv.org/abs/1310.1531

CNN Features off-the-shelf: an Astounding Baseline for Recognition

• intro: CVPR 2014
• arxiv: http://arxiv.org/abs/1403.6382

HD-CNN: Hierarchical Deep Convolutional Neural Network for Image Classification

HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition

• intro: ICCV 2015
• intro: introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy
• project page: https://sites.google.com/site/homepagezhichengyan/home/hdcnn
• arxiv: https://arxiv.org/abs/1410.0736
• code: https://sites.google.com/site/homepagezhichengyan/home/hdcnn/code
• github: https://github.com/stephenyan1231/caffe-public/tree/hdcnn

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

• intro: ImageNet top-5 error: 4.94%
• arxiv: http://arxiv.org/abs/1502.01852
• notes: http://blog.csdn.net/happynear/article/details/45440811

Humans and deep networks largely agree on which kinds of variation make object recognition harder

• arxiv: http://arxiv.org/abs/1604.06486
• review: https://www.technologyreview.com/s/601387/why-machine-vision-is-flawed-in-the-same-way-as-human-vision/

FusionNet: 3D Object Classification Using Multiple Data Representations

• arxiv: https://arxiv.org/abs/1607.05695

From image recognition to object recognition

• blog: https://www.oreilly.com/ideas/from-image-recognition-to-object-recognition

Deep FisherNet for Object Classification

• arxiv: http://arxiv.org/abs/1608.00182

Factorized Bilinear Models for Image Recognition

• intro: TuSimple
• arxiv: https://arxiv.org/abs/1611.05709
• github(MXNet): https://github.com/lyttonhao/Factorized-Bilinear-Network

Hyperspectral CNN Classification with Limited Training Samples

• arxiv: https://arxiv.org/abs/1611.09007

The More You Know: Using Knowledge Graphs for Image Classification

• intro: CMU. GSNN
• arxiv: https://arxiv.org/abs/1612.04844

MaxMin Convolutional Neural Networks for Image Classification

• paper: http://webia.lip6.fr/~thomen/papers/Blot_ICIP_2016.pdf
• github: https://github.com/karandesai-96/maxmin-cnn

Cost-Effective Active Learning for Deep Image Classification

• intro: TCSVT 2016. Sun Yat-sen University & Guangzhou University
• arxiv: https://arxiv.org/abs/1701.03551

Deep Collaborative Learning for Visual Recognition

https://www.arxiv.org/abs/1703.01229

Convolutional Low-Resolution Fine-Grained Classification

https://arxiv.org/abs/1703.05393

Deep Mixture of Diverse Experts for Large-Scale Visual Recognition

https://arxiv.org/abs/1706.07901

Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition

• intro: BMVC 2017
• arxiv: https://arxiv.org/abs/1707.06335

Multi-object Recognition

Multiple Object Recognition with Visual Attention

• keyword: deep recurrent neural network, reinforcement learning
• arxiv: https://arxiv.org/abs/1412.7755
• github: https://github.com/jrbtaylor/visual-attention

Multiple Instance Learning Convolutional Neural Networks for Object Recognition

• intro: ICPR 2016 Oral
• arxiv: https://arxiv.org/abs/1610.03155

Multi-Label Classification

Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification

• intro: CVPR 2017. University of Science and Technology of China & CUHK
• arxiv: https://arxiv.org/abs/1702.05891
• github(official. Caffe): https://github.com/zhufengx/SRN_multilabel/

Order-Free RNN with Visual Attention for Multi-Label Classification

https://arxiv.org/abs/1707.05495

Face Recognition

DeepID

Deep Learning Face Representation from Predicting 10,000 Classes

• intro: CVPR 2014
• paper: http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf
• github: https://github.com/stdcoutzyx/DeepID_FaceClassify

DeepID2

Deep Learning Face Representation by Joint Identification-Verification

• paper: http://papers.nips.cc/paper/5416-analog-memories-in-a-balanced-rate-based-network-of-e-i-neurons

基于Caffe的DeepID2实现

• 1. http://www.miaoerduo.com/deep-learning/%E5%9F%BA%E4%BA%8Ecaffe%E7%9A%84deepid2%E5%AE%9E%E7%8E%B0%EF%BC%88%E4%B8%8A%EF%BC%89.html
• 1. http://www.miaoerduo.com/deep-learning/%E5%9F%BA%E4%BA%8Ecaffe%E7%9A%84deepid2%E5%AE%9E%E7%8E%B0%EF%BC%88%E4%B8%AD%EF%BC%89.html
• 1. http://www.miaoerduo.com/deep-learning/%E5%9F%BA%E4%BA%8Ecaffe%E7%9A%84deepid2%E5%AE%9E%E7%8E%B0%EF%BC%88%E4%B8%8B%EF%BC%89.html

DeepID2+

Deeply learned face representations are sparse, selective, and robust

• arxiv: http://arxiv.org/abs/1412.1265
• video: http://research.microsoft.com/apps/video/?id=260023
• mirror: http://pan.baidu.com/s/1boufl3x

MobileID

MobileID: Face Model Compression by Distilling Knowledge from Neurons

• intro: AAAI 2016 Oral. CUHK
• intro: MobileID is an extremely fast face recognition system by distilling knowledge from DeepID2
• project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/MobileID.html
• paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/aaai16-face-model-compression.pdf
• github: https://github.com/liuziwei7/mobile-id

DeepFace

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

• intro: CVPR 2014. Facebook AI Research
• paper: https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf
• slides: http://valse.mmcheng.net/ftp/20141126/MingYang.pdf
• github: https://github.com/RiweiChen/DeepFace

Deep Face Recognition

• intro: BMVC 2015
• paper: http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf
• homepage: http://www.robots.ox.ac.uk/~vgg/software/vgg_face/
• github(Keras): https://github.com/rcmalli/keras-vggface

FaceNet

FaceNet: A Unified Embedding for Face Recognition and Clustering

• intro: Google. CVPR 2015
• arxiv: http://arxiv.org/abs/1503.03832
• github(Tensorflow): https://github.com/davidsandberg/facenet
• github(Caffe): https://github.com/hizhangp/triplet

Real time face detection and recognition

• intro: Real time face detection and recognition base on opencv/tensorflow/mtcnn/facenet
• github: https://github.com/shanren7/real_time_face_recognition

Targeting Ultimate Accuracy: Face Recognition via Deep Embedding

• intro: CVPR 2015
• arxiv: http://arxiv.org/abs/1506.07310

Learning Robust Deep Face Representation

• arxiv: https://arxiv.org/abs/1507.04844

A Light CNN for Deep Face Representation with Noisy Labels

• arxiv: https://arxiv.org/abs/1511.02683
• github: https://github.com/AlfredXiangWu/face_verification_experiment

Pose-Aware Face Recognition in the Wild

• paper: www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Masi_Pose-Aware_Face_Recognition_CVPR_2016_paper.pdf

Triplet Probabilistic Embedding for Face Verification and Clustering

• intro: Oral Paper in BTAS 2016; NVIDIA Best paper Award
• arxiv: https://arxiv.org/abs/1604.05417
• github(Keras): https://github.com/meownoid/face-identification-tpe

Recurrent Regression for Face Recognition

• arxiv: http://arxiv.org/abs/1607.06999

A Discriminative Feature Learning Approach for Deep Face Recognition

• intro: ECCV 2016
• intro: center loss
• paper: http://ydwen.github.io/papers/WenECCV16.pdf
• github: https://github.com/ydwen/caffe-face
• github: https://github.com/pangyupo/mxnet_center_loss

How Image Degradations Affect Deep CNN-based Face Recognition?

• arxiv: http://arxiv.org/abs/1608.05246

VIPLFaceNet / SeetaFace Engine

VIPLFaceNet: An Open Source Deep Face Recognition SDK

• arxiv: http://arxiv.org/abs/1609.03892

SeetaFace Engine

• intro: SeetaFace Engine is an open source C++ face recognition engine, which can run on CPU with no third-party dependence.
• github: https://github.com/seetaface/SeetaFaceEngine

A Discriminative Feature Learning Approach for Deep Face Recognition

• intro: ECCV 2016
• paper: http://ydwen.github.io/papers/WenECCV16.pdf

Sparsifying Neural Network Connections for Face Recognition

• paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr16.pdf

Range Loss for Deep Face Recognition with Long-tail

• arxiv: https://arxiv.org/abs/1611.08976

Hybrid Deep Learning for Face Verification

• intro: TPAMI 2016. CNN+RBM
• paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTpami16.pdf

Towards End-to-End Face Recognition through Alignment Learning

• intro: Tsinghua University
• arxiv: https://arxiv.org/abs/1701.07174

Multi-Task Convolutional Neural Network for Face Recognition

• arxiv: https://arxiv.org/abs/1702.04710

NormFace: L2 Hypersphere Embedding for Face Verification

• arxiv: https://arxiv.org/abs/1704.06369
• github: https://github.com/happynear/NormFace

SphereFace: Deep Hypersphere Embedding for Face Recognition

• intro: CVPR 2017
• arxiv: http://wyliu.com/papers/LiuCVPR17.pdf
• github: https://github.com/wy1iu/sphereface
• demo: http://v-wb.youku.com/v_show/id_XMjk3NTc1NjMxMg==.html

L2-constrained Softmax Loss for Discriminative Face Verification

https://arxiv.org/abs/1703.09507

Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture

• intro: Amirkabir University of Technology & MIT
• arxiv: https://arxiv.org/abs/1706.06247

Enhancing Convolutional Neural Networks for Face Recognition with Occlusion Maps and Batch Triplet Loss

https://arxiv.org/abs/1707.07923

Video Face Recognition

Attention-Set based Metric Learning for Video Face Recognition

https://arxiv.org/abs/1704.03805

Projects

Using MXNet for Face-related Algorithm

• github: https://github.com/tornadomeet/mxnet-face

clmtrackr: Javascript library for precise tracking of facial features via Constrained Local Models

• github: https://github.com/auduno/clmtrackr
• blog: http://auduno.com/post/61888277175/fitting-faces
• demo: http://auduno.github.io/clmtrackr/examples/facesubstitution.html
• demo: http://auduno.github.io/clmtrackr/face_deformation_video.html
• demo: http://auduno.github.io/clmtrackr/examples/clm_emotiondetection.html
• demo: http://auduno.com/post/84214587523/twisting-faces

DeepLogo

• intro: A brand logo recognition system using deep convolutional neural networks.
• github: https://github.com/satojkovic/DeepLogo

Deep-Leafsnap

• intro: LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.
• github: https://github.com/sujithv28/Deep-Leafsnap

OpenFace

OpenFace: Face Recognition with Deep Neural Networks

• homepage: http://cmusatyalab.github.io/openface/
• github: https://github.com/cmusatyalab/openface
• github: https://github.com/aybassiouny/OpenFaceCpp

OpenFace 0.2.0: Higher accuracy and halved execution time

• homepage: http://bamos.github.io/2016/01/19/openface-0.2.0/

OpenFace: A general-purpose face recognition library with mobile applications

• paper: http://reports-archive.adm.cs.cmu.edu/anon/anon/usr0/ftp/2016/CMU-CS-16-118.pdf

FaceVerification: An Experimental Implementation of Face Verification, 96.8% on LFW

• github: https://github.com/happynear/FaceVerification

OpenFace: an open source facial behavior analysis toolkit

• intro: a state-of-the art open source tool intended for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.
• github: https://github.com/TadasBaltrusaitis/OpenFace

Resources

Face-Resources

• github: https://github.com/betars/Face-Resources

Fine-grained Recognition

Bilinear CNN Models for Fine-grained Visual Recognition

• intro: ICCV 2015
• homepage: http://vis-www.cs.umass.edu/bcnn/
• paper: http://vis-www.cs.umass.edu/bcnn/docs/bcnn_iccv15.pdf
• arxiv: http://arxiv.org/abs/1504.07889
• bitbucket: https://bitbucket.org/tsungyu/bcnn.git

Fine-grained Image Classification by Exploring Bipartite-Graph Labels

• intro: CVPR 2016
• project page: http://www.f-zhou.com/fg.html
• arxiv: http://arxiv.org/abs/1512.02665
• demo: http://www.f-zhou.com/fg_demo/

Embedding Label Structures for Fine-Grained Feature Representation

• intro: CVPR 2016
• arxiv: http://arxiv.org/abs/1512.02895
• paper: http://webpages.uncc.edu/~szhang16/paper/CVPR16_structured_labels.pdf

Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop

• arxiv: http://arxiv.org/abs/1512.05227

Fully Convolutional Attention Localization Networks: Efficient Attention Localization for Fine-Grained Recognition

• arxiv: http://arxiv.org/abs/1603.06765

Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition

• arxiv: https://arxiv.org/abs/1605.06217

Learning Deep Representations of Fine-grained Visual Descriptions

• intro: CVPR 2016
• arxiv: http://arxiv.org/abs/1605.05395
• github: https://github.com/reedscot/cvpr2016

IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1606.03238

Picking Deep Filter Responses for Fine-grained Image Recognition

• intro: CVPR 2016

SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-grained Recognition

• intro: CVPR 2016

Part-Stacked CNN for Fine-Grained Visual Categorization

• intro: CVPR 2016

Fine-grained Recognition in the Noisy Wild: Sensitivity Analysis of Convolutional Neural Networks Approaches

• intro: BMVC 2016
• arxiv: https://arxiv.org/abs/1610.06756

Low-rank Bilinear Pooling for Fine-Grained Classification

• intro: CVPR 2017
• project page: http://www.ics.uci.edu/~skong2/lr_bilinear.html
• arxiv: https://arxiv.org/abs/1611.05109
• github: https://github.com/aimerykong/Low-Rank-Bilinear-Pooling

细粒度图像分析

• intro: by 吴建鑫, NJU. VALSE 2017 Annual Progress Review Series
• slides: http://mac.xmu.edu.cn/valse2017/ppt/APR/wjx_APR.pdf

Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-grained Image Recognition

• intro: CVPR 2017
• paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Fu_Look_Closer_to_CVPR_2017_paper.pdf

Food Recognition

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

• arxiv: http://arxiv.org/abs/1606.05675

Im2Calories: towards an automated mobile vision food diary

• intro: recognize the contents of your meal from a single image, then predict its nutritional contents, such as calories
• paper: http://www.cs.ubc.ca/~murphyk/Papers/im2calories_iccv15.pdf

Food Image Recognition by Using Convolutional Neural Networks (CNNs)

• arxiv: https://arxiv.org/abs/1612.00983

Wide-Slice Residual Networks for Food Recognition

• arxiv: https://arxiv.org/abs/1612.06543

Food Classification with Deep Learning in Keras / Tensorflow

• blog: http://blog.stratospark.com/deep-learning-applied-food-classification-deep-learning-keras.html
• github: https://github.com/stratospark/food-101-keras

ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition

https://arxiv.org/abs/1705.02743

Computer vision-based food calorie estimation: dataset, method, and experiment

https://arxiv.org/abs/1705.07632

Deep Learning-Based Food Calorie Estimation Method in Dietary Assessment

https://arxiv.org/abs/1706.04062

Food Ingredients Recognition through Multi-label Learning

https://arxiv.org/abs/1707.08816

Attribute Recognition

Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios

• keywords: DeepSAR / DeepMAR
• paper: http://or.nsfc.gov.cn/bitstream/00001903-5/417802/1/1000014103914.pdf
• github: https://github.com/kyu-sz/DeepMAR_deploy

Robust Pedestrian Attribute Recognition for an Unbalanced Dataset using Mini-batch Training with Rarity Rate

• intro: Intelligent Vehicles Symposium 2016. Chubu University & Nagoya University, Japan
• paper: http://www.vision.cs.chubu.ac.jp/MPRG/C_group/C081_fukui2016.pdf

Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization

• arxiv: https://arxiv.org/abs/1611.05603
• github: https://github.com/kyu-sz/WPAL-network

Generative Adversarial Models for People Attribute Recognition in Surveillance

• intro: AVSS 2017 oral
• arxiv: https://arxiv.org/abs/1707.02240

A Jointly Learned Deep Architecture for Facial Attribute Analysis and Face Detection in the Wild

https://arxiv.org/abs/1707.08705

Instrument Recognition

Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks

• arxiv: http://arxiv.org/abs/1511.05520
• github: https://github.com/glennq/instrument-recognition

Musical Instrument Recognition

Deep Convolutional Networks on the Pitch Spiral for Musical Instrument Recognition

• paper: https://github.com/lostanlen/ismir2016/blob/master/paper/lostanlen_ismir2016.pdf
• github: https://github.com/lostanlen/ismir2016

Clothes Recognition

DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

• intro: CVPR 2016. FashionNet
• project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/DeepFashion.html
• paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.pdf

Multi-Task Curriculum Transfer Deep Learning of Clothing Attributes

• arxiv: https://arxiv.org/abs/1610.03670

Star-galaxy Classification

Star-galaxy Classification Using Deep Convolutional Neural Networks

• intro: MNRAS
• arxiv: http://arxiv.org/abs/1608.04369
• github: https://github.com/EdwardJKim/dl4astro

Logo Recognition

Deep Learning for Logo Recognition

• arxiv: https://arxiv.org/abs/1701.02620

Plant Classification

Large-Scale Plant Classification with Deep Neural Networks

• intro: Published at Proocedings of ACM Computing Frontiers Conference 2017
• arxiv: https://arxiv.org/abs/1706.03736

Scene Recognition / Scene Classification

Learning Deep Features for Scene Recognition using Places Database

• paper: http://places.csail.mit.edu/places_NIPS14.pdf
• gihtub: https://github.com/metalbubble/places365

Using neon for Scene Recognition: Mini-Places2

• intro: This is an implementation of the deep residual network used for Mini-Places2 as described in He et. al., “Deep Residual Learning for Image Recognition”.
• blog: http://www.nervanasys.com/using-neon-for-scene-recognition-mini-places2/
• github: https://github.com/hunterlang/mpmz

Scene Classification with Inception-7

• slides: http://lsun.cs.princeton.edu/slides/Christian.pdf

Semantic Clustering for Robust Fine-Grained Scene Recognition

• arxiv: http://arxiv.org/abs/1607.07614

Leaderboard

Leaderboard of Places Database

• intro: currently rank1: Qian Zhang(Beijing Samsung Telecom R&D Center), 0.6410@top1, 0.9065@top5
• homepage: http://places.csail.mit.edu/user/leaderboard.php

Blogs

What is the class of this image ? - Discover the current state of the art in objects classification

• intro: “Discover the current state of the art in objects classification.”
• intro: MNIST, CIFAR-10, CIFAR-100, STL-10, SVHN, ILSVRC2012 task 1
• blog: http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html

Object Recognition with Convolutional Neural Networks in the Keras Deep Learning Library

http://machinelearningmastery.com/object-recognition-convolutional-neural-networks-keras-deep-learning-library/

The Effect of Resolution on Deep Neural Network Image Classification Accuracy

https://medium.com/the-downlinq/the-effect-of-resolution-on-deep-neural-network-image-classification-accuracy-d1338e2782c5#.em5rk991r