Papers

--- author: David Zhang category: deep_learning title: Image Retrieval date: 2019-12-21 --- # Papers **Using Very Deep Autoencoders for Content-Based Image Retrieval** - intro: ESANN 2011. Alex Krizhevsky, and Geoffrey E. Hinton - paper: [https://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf](https://www.cs.toronto.edu/~hinton/absps/esann-deep-final.pdf) - paper: [http://www.cs.toronto.edu/~fritz/absps/esann-deep-final.pdf](http://www.cs.toronto.edu/~fritz/absps/esann-deep-final.pdf) **Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data** - arxiv: [http://arxiv.org/abs/1312.4740](http://arxiv.org/abs/1312.4740) - paper: [http://legacy.openreview.net/document/90fc8dad-ad02-4ddc-ab06-e7b55706869d#90fc8dad-ad02-4ddc-ab06-e7b55706869d](http://legacy.openreview.net/document/90fc8dad-ad02-4ddc-ab06-e7b55706869d#90fc8dad-ad02-4ddc-ab06-e7b55706869d) **Neural Codes for Image Retrieval** ![](http://sites.skoltech.ru/app/data/uploads/sites/25/2014/11/example-e1404721339557.png) - intro: ECCV 2014 - project page: [http://sites.skoltech.ru/compvision/projects/neuralcodes/](http://sites.skoltech.ru/compvision/projects/neuralcodes/) - arxiv: [http://arxiv.org/abs/1404.1777](http://arxiv.org/abs/1404.1777) - github: [https://github.com/arbabenko/Spoc](https://github.com/arbabenko/Spoc) **Efficient On-the-fly Category Retrieval using ConvNets and GPUs** - arxiv: [http://arxiv.org/abs/1407.4764](http://arxiv.org/abs/1407.4764) **Learning visual similarity for product design with convolutional neural networks** - intro: SIGGRAPH 2015 - paper: [http://www.cs.cornell.edu/~kb/publications/SIG15ProductNet.pdf](http://www.cs.cornell.edu/~kb/publications/SIG15ProductNet.pdf) - paper: [http://dl.acm.org.sci-hub.cc/citation.cfm?doid=2809654.2766959](http://dl.acm.org.sci-hub.cc/citation.cfm?doid=2809654.2766959) **Exploiting Local Features from Deep Networks for Image Retrieval** - intro: CVPR DeepVision Workshop 2015 - arxiv: [https://arxiv.org/abs/1504.05133](https://arxiv.org/abs/1504.05133) **Visual Search at Pinterest** - intro: in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge and Discovery and Data Mining, 2015 - arxiv: [http://arxiv.org/abs/1505.07647](http://arxiv.org/abs/1505.07647) - blog: [https://engineering.pinterest.com/blog/introducing-new-way-visually-search-pinterest](https://engineering.pinterest.com/blog/introducing-new-way-visually-search-pinterest) **Aggregating Deep Convolutional Features for Image Retrieval** - intro: ICCV 2015 - intro: Sum pooing - arxiv: [http://arxiv.org/abs/1510.07493](http://arxiv.org/abs/1510.07493) **Particular object retrieval with integral max-pooling of CNN activations** - intro: use max-pooling to aggregate the deep descriptors, R-MAC (regional maximum activation of convolutions) - arxiv: [https://arxiv.org/abs/1511.05879](https://arxiv.org/abs/1511.05879) **Group Invariant Deep Representations for Image Instance Retrieval** - arxiv: [http://arxiv.org/abs/1601.02093](http://arxiv.org/abs/1601.02093) **Where to Buy It: Matching Street Clothing Photos in Online Shops** ![](http://www.tamaraberg.com/street2shop/header.jpg) - intro: ICCV 2015 - hmepage: [http://www.tamaraberg.com/street2shop/](http://www.tamaraberg.com/street2shop/) - paper: [http://www.tamaraberg.com/papers/street2shop.pdf](http://www.tamaraberg.com/papers/street2shop.pdf) - paper: [http://www.cv-foundation.org/openaccess/content_iccv_2015/html/Kiapour_Where_to_Buy_ICCV_2015_paper.html](http://www.cv-foundation.org/openaccess/content_iccv_2015/html/Kiapour_Where_to_Buy_ICCV_2015_paper.html) **Natural Language Object Retrieval** ![](http://ronghanghu.com/wp-content/uploads/method-900x353.png) - intro: CVPR 2015 - homepage: [http://ronghanghu.com/text_obj_retrieval/](http://ronghanghu.com/text_obj_retrieval/) - arxiv: [http://arxiv.org/abs/1511.04164](http://arxiv.org/abs/1511.04164) - slides: [http://ronghanghu.com/slides/cvpr16_text_obj_retrieval_slides.pdf](http://ronghanghu.com/slides/cvpr16_text_obj_retrieval_slides.pdf) - github: [https://github.com/ronghanghu/natural-language-object-retrieval](https://github.com/ronghanghu/natural-language-object-retrieval) - github: [https://github.com/andrewliao11/Natural-Language-Object-Retrieval-tensorflow](https://github.com/andrewliao11/Natural-Language-Object-Retrieval-tensorflow) **Deep Image Retrieval: Learning global representations for image search** - intro: ECCV 2016 - project page: [http://www.xrce.xerox.com/Research-Development/Computer-Vision/Learning-Visual-Representations/Deep-Image-Retrieval](http://www.xrce.xerox.com/Research-Development/Computer-Vision/Learning-Visual-Representations/Deep-Image-Retrieval) - arxiv: [https://arxiv.org/abs/1604.01325](https://arxiv.org/abs/1604.01325) - slides: [http://www.slideshare.net/xavigiro/deep-image-retrieval-learning-global-representations-for-image-search](http://www.slideshare.net/xavigiro/deep-image-retrieval-learning-global-representations-for-image-search) **End-to-end Learning of Deep Visual Representations for Image Retrieval** - intro: IJCV 2017. Extended version of our ECCV2016 paper "Deep Image Retrieval: Learning global representations for image search" - project page: [http://www.xrce.xerox.com/Research-Development/Computer-Vision/Learning-Visual-Representations/Deep-Image-Retrieval](http://www.xrce.xerox.com/Research-Development/Computer-Vision/Learning-Visual-Representations/Deep-Image-Retrieval) - arxiv: [https://arxiv.org/abs/1610.07940](https://arxiv.org/abs/1610.07940) **Bags of Local Convolutional Features for Scalable Instance Search** - intro: ICMR 2016. Best Poster Award at ICMR 2016. - project page: [https://imatge-upc.github.io/retrieval-2016-icmr/](https://imatge-upc.github.io/retrieval-2016-icmr/) - arxiv: [https://arxiv.org/abs/1604.04653](https://arxiv.org/abs/1604.04653) - github: [https://github.com/imatge-upc/retrieval-2016-icmr](https://github.com/imatge-upc/retrieval-2016-icmr) - slides: [http://www.slideshare.net/xavigiro/convolutional-features-for-instance-search](http://www.slideshare.net/xavigiro/convolutional-features-for-instance-search) **Faster R-CNN Features for Instance Search** - intro: DeepVision Workshop in CVPR 2016 - homepage: [http://imatge-upc.github.io/retrieval-2016-deepvision/](http://imatge-upc.github.io/retrieval-2016-deepvision/) - arxiv: [http://arxiv.org/abs/1604.08893](http://arxiv.org/abs/1604.08893) - github: [https://github.com/imatge-upc/retrieval-2016-deepvision](https://github.com/imatge-upc/retrieval-2016-deepvision) **Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps** - intro: query adaptive matching (QAM), Feature Map Pooling, Overlapped Spatial Pyramid Pooling (OSPP) - arxiv: [https://arxiv.org/abs/1606.06811](https://arxiv.org/abs/1606.06811) **Adversarial Training For Sketch Retrieval** - arxiv: [http://arxiv.org/abs/1607.02748](http://arxiv.org/abs/1607.02748) **Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks** - intro: CVPR 2016. DeepBit - paper: [http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Lin_Learning_Compact_Binary_CVPR_2016_paper.pdf](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Lin_Learning_Compact_Binary_CVPR_2016_paper.pdf) - github: [https://github.com/kevinlin311tw/cvpr16-deepbit](https://github.com/kevinlin311tw/cvpr16-deepbit) **Fast Training of Triplet-based Deep Binary Embedding Networks** - intro: CVPR 2016 - arxiv: [https://arxiv.org/abs/1603.02844](https://arxiv.org/abs/1603.02844) - paper: [http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhuang_Fast_Training_of_CVPR_2016_paper.pdf](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhuang_Fast_Training_of_CVPR_2016_paper.pdf) - bitbucket(official): [https://bitbucket.org/jingruixiaozhuang/fast-training-of-triplet-based-deep-binary-embedding-networks](https://bitbucket.org/jingruixiaozhuang/fast-training-of-triplet-based-deep-binary-embedding-networks) **Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles** - intro: CVPR 2016 - intro: vehicle re-identification, vehicle retrieval. coupled clusters loss - paper: [http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_Deep_Relative_Distance_CVPR_2016_paper.pdf](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_Deep_Relative_Distance_CVPR_2016_paper.pdf) **DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations** ![](http://personal.ie.cuhk.edu.hk/~lz013/projects/deepfashion/intro.png) - intro: CVPR 2016. FashionNet - project page: [http://personal.ie.cuhk.edu.hk/~lz013/projects/DeepFashion.html](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](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.pdf) **CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples** ![](http://ptak.felk.cvut.cz/personal/radenfil/siamac/siamac.png) - intro: ECCV 2016 - project page(paper+code+data): [http://cmp.felk.cvut.cz/~radenfil/projects/siamac.html](http://cmp.felk.cvut.cz/~radenfil/projects/siamac.html) - arxiv: [https://arxiv.org/abs/1604.02426](https://arxiv.org/abs/1604.02426) - paper: [http://cmp.felk.cvut.cz/~radenfil/publications/Radenovic-ECCV16.pdf](http://cmp.felk.cvut.cz/~radenfil/publications/Radenovic-ECCV16.pdf) - code(Matlab): [http://ptak.felk.cvut.cz/personal/radenfil/siamac/siaMAC_code.tar.gz](http://ptak.felk.cvut.cz/personal/radenfil/siamac/siaMAC_code.tar.gz) **PicHunt: Social Media Image Retrieval for Improved Law Enforcement** - arxiv: [http://arxiv.org/abs/1608.00905](http://arxiv.org/abs/1608.00905) **SIFT Meets CNN: A Decade Survey of Instance Retrieval** - arxiv: [http://arxiv.org/abs/1608.01807](http://arxiv.org/abs/1608.01807) **The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies** - project page: [http://sketchy.eye.gatech.edu/](http://sketchy.eye.gatech.edu/) - paper: [http://www.cc.gatech.edu/~hays/tmp/sketchy-database.pdf](http://www.cc.gatech.edu/~hays/tmp/sketchy-database.pdf) - github: [https://github.com/janesjanes/sketchy](https://github.com/janesjanes/sketchy) **What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?** - arxiv: [https://arxiv.org/abs/1611.01640](https://arxiv.org/abs/1611.01640) **Image Retrieval with Deep Local Features and Attention-based Keypoints** - arxiv: [https://arxiv.org/abs/1612.05478](https://arxiv.org/abs/1612.05478) **Internet-Based Image Retrieval Using End-to-End Trained Deep Distributions** - arxiv: [https://arxiv.org/abs/1612.07697](https://arxiv.org/abs/1612.07697) **Compression of Deep Neural Networks for Image Instance Retrieval** - intro: DCC 2017 - arxiv: [https://arxiv.org/abs/1701.04923](https://arxiv.org/abs/1701.04923) **Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval** - arxiv: [https://arxiv.org/abs/1701.05003](https://arxiv.org/abs/1701.05003) **Siamese Network of Deep Fisher-Vector Descriptors for Image Retrieval** - arxiv: [https://arxiv.org/abs/1702.00338](https://arxiv.org/abs/1702.00338) **Deep Geometric Retrieval** - arxiv: [https://arxiv.org/abs/1702.06383](https://arxiv.org/abs/1702.06383) **Context Aware Query Image Representation for Particular Object Retrieval** [https://www.arxiv.org/abs/1703.01226](https://www.arxiv.org/abs/1703.01226) **An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning** [https://arxiv.org/abs/1703.07579](https://arxiv.org/abs/1703.07579) **AMC: Attention guided Multi-modal Correlation Learning for Image Search** - intro: CVPR 2017 - arxiv: [https://arxiv.org/abs/1704.00763](https://arxiv.org/abs/1704.00763) - github: [https://github.com/kanchen-usc/amc_att](https://github.com/kanchen-usc/amc_att) **Video2Shop: Exactly Matching Clothes in Videos to Online Shopping Images** - intro: CVPR 2017 - keywrods: AsymNet - arxiv: [https://arxiv.org/abs/1804.05287](https://arxiv.org/abs/1804.05287) **Deep image representations using caption generators** - intro: ICME 2017 - arxiv: [https://arxiv.org/abs/1705.09142](https://arxiv.org/abs/1705.09142) **Visual Search at eBay** - intro: 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017 - arxiv: [https://arxiv.org/abs/1706.03154](https://arxiv.org/abs/1706.03154) **Sampling Matters in Deep Embedding Learning** - intro: UT Austin & A9/Amazon - keywords: distance weighted sampling - arxiv: [https://arxiv.org/abs/1706.07567](https://arxiv.org/abs/1706.07567) **One-Shot Fine-Grained Instance Retrieval** - intro: ACM MM 2017 - arxiv: [https://arxiv.org/abs/1707.00811](https://arxiv.org/abs/1707.00811) **Selective Deep Convolutional Features for Image Retrieval** - intro: ACM MM 2017 - arxiv: [https://arxiv.org/abs/1707.00809](https://arxiv.org/abs/1707.00809) **Class-Weighted Convolutional Features for Visual Instance Search** - intro: BMVC 2017. Universitat Politecnica de Catalunya Barcelona & CSIRO - project page: [http://imatge-upc.github.io/retrieval-2017-cam/](http://imatge-upc.github.io/retrieval-2017-cam/) - arxiv: [https://arxiv.org/abs/1707.02581](https://arxiv.org/abs/1707.02581) - github: [https://github.com/imatge-upc/retrieval-2017-cam](https://github.com/imatge-upc/retrieval-2017-cam) **Learning a Repression Network for Precise Vehicle Search** [https://arxiv.org/abs/1708.02386](https://arxiv.org/abs/1708.02386) **SUBIC: A supervised, structured binary code for image search** - intro: ICCV 2017 (Spotlight). Technicolor & INRIA Rennes & Amazon - arxiv: [https://arxiv.org/abs/1708.02932](https://arxiv.org/abs/1708.02932) **Pruning Convolutional Neural Networks for Image Instance Retrieval** [https://arxiv.org/abs/1707.05455](https://arxiv.org/abs/1707.05455) **Image2song: Song Retrieval via Bridging Image Content and Lyric Words** - intro: ICCV 2017. Chinese Academy of Sciences & Northwestern Polytechnical University - arxiv: [https://arxiv.org/abs/1708.05851](https://arxiv.org/abs/1708.05851) **Region-Based Image Retrieval Revisited** - intro: ACM Multimedia 2017 (Oral) - arxiv: [https://arxiv.org/abs/1709.09106](https://arxiv.org/abs/1709.09106) **Beyond Part Models: Person Retrieval with Refined Part Pooling** [https://arxiv.org/abs/1711.09349](https://arxiv.org/abs/1711.09349) **Query-Adaptive R-CNN for Open-Vocabulary Object Detection and Retrieval** [https://arxiv.org/abs/1711.09509](https://arxiv.org/abs/1711.09509) **Saliency Weighted Convolutional Features for Instance Search** - intro: Dublin City University & Universitat Politecnica de Catalunya - keywords: local convolutional features (BLCF), human visual attention models (saliency) - project page: [https://imatge-upc.github.io/salbow/](https://imatge-upc.github.io/salbow/) - arxiv: [https://arxiv.org/abs/1711.10795](https://arxiv.org/abs/1711.10795) - github: [https://arxiv.org/abs/1711.10795](https://arxiv.org/abs/1711.10795) **DeepStyle: Multimodal Search Engine for Fashion and Interior Design** [https://arxiv.org/abs/1801.03002](https://arxiv.org/abs/1801.03002) **From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval** [https://arxiv.org/abs/1802.02899](https://arxiv.org/abs/1802.02899) **Web-Scale Responsive Visual Search at Bing** - intro: Microsoft - arxiv: [https://arxiv.org/abs/1802.04914](https://arxiv.org/abs/1802.04914) **Approximate Query Matching for Image Retrieval** [https://arxiv.org/abs/1803.05401](https://arxiv.org/abs/1803.05401) **Object Captioning and Retrieval with Natural Language** [https://arxiv.org/abs/1803.06152](https://arxiv.org/abs/1803.06152) **Triplet-Center Loss for Multi-View 3D Object Retrieval** - intro: CVPR 2018 - arxiv: [https://arxiv.org/abs/1803.06189](https://arxiv.org/abs/1803.06189) **Collaborative Multi-modal deep learning for the personalized product retrieval in Facebook Marketplace** - intro: Facebook = arxiv: [https://arxiv.org/abs/1805.12312](https://arxiv.org/abs/1805.12312) **DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval** - intro: ICPR 2018 - arxiv: [https://arxiv.org/abs/1806.02984](https://arxiv.org/abs/1806.02984) - github: [https://github.com/jdhao/deep_firearm](https://github.com/jdhao/deep_firearm) **Instance Search via Instance Level Segmentation and Feature Representation** [https://arxiv.org/abs/1806.03576](https://arxiv.org/abs/1806.03576) **Deep Feature Aggregation with Heat Diffusion for Image Retrieval** - arxiv: [https://arxiv.org/abs/1805.08587](https://arxiv.org/abs/1805.08587) - github: [https://github.com/pangsm0415/HeW](https://github.com/pangsm0415/HeW) **Single Shot Scene Text Retrieval** - intro: ECCV 2018 - arxiv: [https://arxiv.org/abs/1808.09044](https://arxiv.org/abs/1808.09044) **Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling** - intro: Yahoo Research - arxiv: [https://arxiv.org/abs/1810.04652](https://arxiv.org/abs/1810.04652) **Attention-aware Generalized Mean Pooling for Image Retrieval** [https://arxiv.org/abs/1811.00202](https://arxiv.org/abs/1811.00202) **Hierarchy-based Image Embeddings for Semantic Image Retrieval** - intro: WACV 2019 - arxiv: [https://arxiv.org/abs/1809.09924](https://arxiv.org/abs/1809.09924) - github: [https://github.com/cvjena/semantic-embeddings](https://github.com/cvjena/semantic-embeddings) ** **Mean Local Group Average Precision (mLGAP): A New Performance Metric for Hashing-based Retrieval** [https://arxiv.org/abs/1811.09763](https://arxiv.org/abs/1811.09763) **Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network** [https://arxiv.org/abs/1811.11375](https://arxiv.org/abs/1811.11375) **Detect-to-Retrieve: Efficient Regional Aggregation for Image Search** - intro: University of Cambridge & Google AI - arxiv: [https://arxiv.org/abs/1812.01584](https://arxiv.org/abs/1812.01584) **Learning with Average Precision: Training Image Retrieval with a Listwise Loss** - intro: NAVER LABS Europe - arxiv: [https://arxiv.org/abs/1906.07589](https://arxiv.org/abs/1906.07589) **A Benchmark on Tricks for Large-scale Image Retrieval** - arxiv: [https://arxiv.org/abs/1907.11854](https://arxiv.org/abs/1907.11854) # Hashing **Supervised Hashing for Image Retrieval via Image Representation Learning** - intro: AAAI 2014. Sun Yat-Sen University & National University of Singapore - keywords: CNNH (Convolutional Neural Network Hashing) - paper: [www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/download/8137/8861](www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/download/8137/8861) - slides: [https://pdfs.semanticscholar.org/f633/8f23860f9c4808586bbc7e8907d33836147f.pdf](https://pdfs.semanticscholar.org/f633/8f23860f9c4808586bbc7e8907d33836147f.pdf) **Simultaneous Feature Learning and Hash Coding with Deep Neural Networks** - intro: CVPR 2015. Sun Yat-Sen University & National University of Singapore - keywords: NINH (NIN Hashing), DNNH (Deep Neural Network Hashing) - arxiv: [https://arxiv.org/abs/1504.03410](https://arxiv.org/abs/1504.03410) - paper: [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lai_Simultaneous_Feature_Learning_2015_CVPR_paper.pdf](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lai_Simultaneous_Feature_Learning_2015_CVPR_paper.pdf) **Hashing by Deep Learning** - intro: IBM T. J. Watson Research Center - paper: [http://www.ee.columbia.edu/~wliu/WeiLiu_DLHash.pdf](http://www.ee.columbia.edu/~wliu/WeiLiu_DLHash.pdf) **Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval** - intro: CVPR 2015. DSRH (Deep Semantic Ranking Hashing) - arxiv: [http://arxiv.org/abs/1501.06272](http://arxiv.org/abs/1501.06272) **Deep Learning of Binary Hash Codes for Fast Image Retrieval** - intro: CVPR Workshop 2015 - keywords: MNIST, CIFAR-10, Yahoo-1M. DLBHC (Deep Learning of Binary Hash Codes) - paper: [http://www.iis.sinica.edu.tw/~kevinlin311.tw/cvprw15.pdf](http://www.iis.sinica.edu.tw/~kevinlin311.tw/cvprw15.pdf) - github: [https://github.com/kevinlin311tw/caffe-cvprw15](https://github.com/kevinlin311tw/caffe-cvprw15) **Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search** - intro: SSDH - arxiv: [http://arxiv.org/abs/1507.00101](http://arxiv.org/abs/1507.00101) - github: [https://github.com/kevinlin311tw/Caffe-DeepBinaryCode](https://github.com/kevinlin311tw/Caffe-DeepBinaryCode) **Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification** - intro: IEEE Transactions on Image Processing 2015 - keywords: DRSCH (Deep Regularized Similarity Comparison Hashing) - project page: [http://vision.sysu.edu.cn/projects/deephashing/](http://vision.sysu.edu.cn/projects/deephashing/) - arxiv: [https://arxiv.org/abs/1508.04535](https://arxiv.org/abs/1508.04535) - github: [https://github.com/ruixuejianfei/BitScalableDeepHash](https://github.com/ruixuejianfei/BitScalableDeepHash) **Deep Supervised Hashing for Fast Image Retrieval** - intro: CVPR 2016 - keywords: DSH (Deep Supervised Hashing) - paper: [http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_Deep_Supervised_Hashing_CVPR_2016_paper.pdf](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_Deep_Supervised_Hashing_CVPR_2016_paper.pdf) - paper: [http://www.jdl.ac.cn/doc/2011/201711214443668218_deep%20supervised%20hashing%20for%20fast%20image%20retrieval_cvpr2016.pdf](http://www.jdl.ac.cn/doc/2011/201711214443668218_deep%20supervised%20hashing%20for%20fast%20image%20retrieval_cvpr2016.pdf) - github: [https://github.com/lhmRyan/deep-supervised-hashing-DSH](https://github.com/lhmRyan/deep-supervised-hashing-DSH) **Deep Hashing Network for Efficient Similarity Retrieval** - intro: AAAI 2016 - paper: [http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12039](http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12039) **Feature Learning based Deep Supervised Hashing with Pairwise Labels** - intro: IJCAI 2016 - arxiv: [https://arxiv.org/abs/1511.03855](https://arxiv.org/abs/1511.03855) - paper: [https://www.ijcai.org/Proceedings/16/Papers/245.pdf](https://www.ijcai.org/Proceedings/16/Papers/245.pdf) - paper: [https://cs.nju.edu.cn/lwj/paper/IJCAI16_DPSH.pdf](https://cs.nju.edu.cn/lwj/paper/IJCAI16_DPSH.pdf) - code: [http://cs.nju.edu.cn/lwj/code/DPSH_code.rar](http://cs.nju.edu.cn/lwj/code/DPSH_code.rar) **Deep Cross-Modal Hashing** [https://arxiv.org/abs/1602.02255](https://arxiv.org/abs/1602.02255) **Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval** [https://arxiv.org/abs/1804.11013](https://arxiv.org/abs/1804.11013) **SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval** - arxiv: [http://arxiv.org/abs/1607.08477](http://arxiv.org/abs/1607.08477) **Deep Semantic-Preserving and Ranking-Based Hashing for Image Retrieval** - intro: Microsoft - paper: [http://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/Deep-Semantic-Preserving-and-Ranking-Based-Hashing-for-Image-Retrieval.pdf](http://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/Deep-Semantic-Preserving-and-Ranking-Based-Hashing-for-Image-Retrieval.pdf) **Deep Hashing: A Joint Approach for Image Signature Learning** - arxiv: [http://arxiv.org/abs/1608.03658](http://arxiv.org/abs/1608.03658) **Transitive Hashing Network for Heterogeneous Multimedia Retrieval** - intro: state of the art on NUS-WIDE, ImageNet-YahooQA - arxiv: [http://arxiv.org/abs/1608.04307](http://arxiv.org/abs/1608.04307) **Deep Residual Hashing** - arxiv: [https://arxiv.org/abs/1612.05400](https://arxiv.org/abs/1612.05400) **Deep Region Hashing for Efficient Large-scale Instance Search from Images** - intro: Columbia University & University of Electronic Science and Technology of China - arxiv: [https://arxiv.org/abs/1701.07901](https://arxiv.org/abs/1701.07901) **HashNet: Deep Learning to Hash by Continuation** - intro: ICCV 2017. Tsinghua University - arxiv: [https://arxiv.org/abs/1702.00758](https://arxiv.org/abs/1702.00758) - github: [https://github.com/thuml/HashNet](https://github.com/thuml/HashNet) **Unsupervised Triplet Hashing for Fast Image Retrieval** - arxiv: [https://www.arxiv.org/abs/1702.08798](https://www.arxiv.org/abs/1702.08798) **Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval** - intro: CVPR 2017 spotlight paper - arxiv: [https://arxiv.org/abs/1703.05605](https://arxiv.org/abs/1703.05605) **Learning Robust Hash Codes for Multiple Instance Image Retrieval** - arxiv: [https://arxiv.org/abs/1703.05724](https://arxiv.org/abs/1703.05724) **Simultaneous Feature Aggregating and Hashing for Large-scale Image Search** - intro: CVPR 2017 - arxiv: [https://arxiv.org/abs/1704.00860](https://arxiv.org/abs/1704.00860) **Learning to Hash** - blog: [https://cs.nju.edu.cn/lwj/L2H.html](https://cs.nju.edu.cn/lwj/L2H.html) **Hashing as Tie-Aware Learning to Rank** [https://arxiv.org/abs/1705.08562](https://arxiv.org/abs/1705.08562) **Deep Hashing Network for Unsupervised Domain Adaptation** - intro: CVPR 2017 - arxiv: [https://arxiv.org/abs/1706.07522](https://arxiv.org/abs/1706.07522) - github(MatConvNet): [https://github.com/hemanthdv/da-hash](https://github.com/hemanthdv/da-hash) **Deep Binary Reconstruction for Cross-modal Hashing** - intro: ACM Multimedia 2017 - arxiv: [https://arxiv.org/abs/1708.05127](https://arxiv.org/abs/1708.05127) **A Revisit on Deep Hashings for Large-scale Content Based Image Retrieval** - intro: Zhejiang University - arixv: [https://arxiv.org/abs/1711.06016](https://arxiv.org/abs/1711.06016) **The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching** - keywords: finegrained sketch-based image retrieval (FG-SBIR) and Person Re-identification (person ReID) - arxiv: [https://arxiv.org/abs/1711.08106](https://arxiv.org/abs/1711.08106) **ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks** [https://arxiv.org/abs/1711.08364](https://arxiv.org/abs/1711.08364) **Supervised Hashing with End-to-End Binary Deep Neural Network** [https://arxiv.org/abs/1711.08901](https://arxiv.org/abs/1711.08901) **Transfer Adversarial Hashing for Hamming Space Retrieval** [https://arxiv.org/abs/1712.04616](https://arxiv.org/abs/1712.04616) **Dual Asymmetric Deep Hashing Learning** [https://arxiv.org/abs/1801.08360](https://arxiv.org/abs/1801.08360) **Attribute-Guided Network for Cross-Modal Zero-Shot Hashing** [https://arxiv.org/abs/1802.01943](https://arxiv.org/abs/1802.01943) **Deep Reinforcement Learning for Image Hashing** [https://arxiv.org/abs/1802.02904](https://arxiv.org/abs/1802.02904) **Hashing with Mutual Information** [https://arxiv.org/abs/1803.00974](https://arxiv.org/abs/1803.00974) **Zero-Shot Sketch-Image Hashing** - intro: CVPR 2018 spotlight - arxiv: [https://arxiv.org/abs/1803.02284](https://arxiv.org/abs/1803.02284) **Instance Similarity Deep Hashing for Multi-Label Image Retrieval** [https://arxiv.org/abs/1803.02987](https://arxiv.org/abs/1803.02987) **Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss** - intro: City University of Hong Kong - arxiv: [https://arxiv.org/abs/1803.04137](https://arxiv.org/abs/1803.04137) **Unsupervised Semantic Deep Hashing** [https://arxiv.org/abs/1803.06911](https://arxiv.org/abs/1803.06911) **SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval** - intro: CVPR 2018 - arxiv: [https://arxiv.org/abs/1804.01401](https://arxiv.org/abs/1804.01401) **Improving Deep Binary Embedding Networks by Order-aware Reweighting of Triplets** - intro: Sun Yat-sen University - arxiv: [https://arxiv.org/abs/1804.06061](https://arxiv.org/abs/1804.06061) **Deep Semantic Hashing with Generative Adversarial Networks** - intro: SIGIR 2017 Oral - arxiv: [https://arxiv.org/abs/1804.08275](https://arxiv.org/abs/1804.08275) **Deep Ordinal Hashing with Spatial Attention** [https://arxiv.org/abs/1805.02459](https://arxiv.org/abs/1805.02459) **Efficient end-to-end learning for quantizable representations** - intro: ICML 2018. Seoul National University - arxiv: [https://arxiv.org/abs/1805.05809](https://arxiv.org/abs/1805.05809) - github: [https://github.com/maestrojeong/Deep-Hash-Table-ICML18](https://github.com/maestrojeong/Deep-Hash-Table-ICML18) **Unsupervised Deep Image Hashing through Tag Embeddings** [https://arxiv.org/abs/1806.05804](https://arxiv.org/abs/1806.05804) **Adversarial Learning for Fine-grained Image Search** [https://arxiv.org/abs/1807.02247](https://arxiv.org/abs/1807.02247) **Error Correction Maximization for Deep Image Hashing** [https://arxiv.org/abs/1808.01942](https://arxiv.org/abs/1808.01942) **Deep Priority Hashing** - intro: ACM MM 2018 Poster - arxiv: [https://arxiv.org/abs/1809.01238](https://arxiv.org/abs/1809.01238) **Neurons Merging Layer: Towards Progressive Redundancy Reduction for Deep Supervised Hashing** [https://arxiv.org/abs/1809.02302](https://arxiv.org/abs/1809.02302) **Deep LDA Hashing** [https://arxiv.org/abs/1810.03402](https://arxiv.org/abs/1810.03402) **Deep Triplet Quantization** - intro: ACM Multimedia 2018 oral - arxiv: [https://arxiv.org/abs/1902.00153](https://arxiv.org/abs/1902.00153) **SADIH: Semantic-Aware DIscrete Hashing** - intro: AAAI 2019 - arxiv: [https://arxiv.org/abs/1904.01739](https://arxiv.org/abs/1904.01739) **Feature Pyramid Hashing** [https://arxiv.org/abs/1904.02325](https://arxiv.org/abs/1904.02325) **Global Hashing System for Fast Image Search** [https://arxiv.org/abs/1904.08685](https://arxiv.org/abs/1904.08685) # Cross Modal Retrieval **Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network** - intro: ICCV 2015 - intro: DARN, cross-entropy loss, triplet loss - arxiv: [http://arxiv.org/abs/1505.07922](http://arxiv.org/abs/1505.07922) **Deep Learning for Content-Based, Cross-Modal Retrieval of Videos and Music** - arxiv: [https://arxiv.org/abs/1704.06761](https://arxiv.org/abs/1704.06761) - supplementary: [https://youtu.be/ZyINqDMo3Fg](https://youtu.be/ZyINqDMo3Fg) **Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval** - intro: ICCV 2017 - arxiv: [https://arxiv.org/abs/1708.02531](https://arxiv.org/abs/1708.02531) **MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval** [https://arxiv.org/abs/1708.04308](https://arxiv.org/abs/1708.04308) **Cross-Domain Image Retrieval with Attention Modeling** [https://arxiv.org/abs/1709.01784](https://arxiv.org/abs/1709.01784) **Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models** [https://arxiv.org/abs/1711.06420](https://arxiv.org/abs/1711.06420) **HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval** [https://arxiv.org/abs/1711.09347](https://arxiv.org/abs/1711.09347) **Objects that Sound** - intro: DeepMind, VGG - arxiv: [https://arxiv.org/abs/1712.06651](https://arxiv.org/abs/1712.06651) **Cross-modal Embeddings for Video and Audio Retrieval** - arxiv: [https://arxiv.org/abs/1801.02200](https://arxiv.org/abs/1801.02200) - github: [https://github.com/surisdi/youtube-8m](https://github.com/surisdi/youtube-8m) **Learnable PINs: Cross-Modal Embeddings for Person Identity** - intro: VGG - arxiv: [https://arxiv.org/abs/1805.00833](https://arxiv.org/abs/1805.00833) **Revisiting Cross Modal Retrieval** - intro: ECCVW (MULA 2018) - arxiv: [https://arxiv.org/abs/1807.07364](https://arxiv.org/abs/1807.07364) ## Projects **HABIR哈希图像检索工具箱** - intro: Various hashing methods for image retrieval and serves as the baselines - blog: [http://yongyuan.name/habir/](http://yongyuan.name/habir/) - github: [https://github.com/willard-yuan/hashing-baseline-for-image-retrieval](https://github.com/willard-yuan/hashing-baseline-for-image-retrieval) # Video Indexing / Retrieval **Face Video Retrieval via Deep Learning of Binary Hash Representations** - paper: [https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/11893/12117](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/11893/12117) **Deep Learning Based Semantic Video Indexing and Retrieval** - arxiv: [https://arxiv.org/abs/1601.07754](https://arxiv.org/abs/1601.07754) **Learning Joint Representations of Videos and Sentences with Web Image Search** - intro: 4th Workshop on Web-scale Vision and Social Media (VSM), ECCV 2016 - arxiv: [http://arxiv.org/abs/1608.02367](http://arxiv.org/abs/1608.02367) **Multi-View Product Image Search Using ConvNets Features** - arxiv: [http://arxiv.org/abs/1608.03462](http://arxiv.org/abs/1608.03462) **Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search** - arxiv: [https://arxiv.org/abs/1611.05301](https://arxiv.org/abs/1611.05301) **Binary Subspace Coding for Query-by-Image Video Retrieval** - arxiv: [https://arxiv.org/abs/1612.01657](https://arxiv.org/abs/1612.01657) **Action Search: Learning to Search for Human Activities in Untrimmed Videos** [https://arxiv.org/abs/1706.04269](https://arxiv.org/abs/1706.04269) **Deep Supervised Hashing with Triplet Labels** - intro: ACCV 2016 - arxiv: [https://arxiv.org/abs/1612.03900](https://arxiv.org/abs/1612.03900) **Supervised Deep Hashing for Hierarchical Labeled Data** - arxiv: [https://arxiv.org/abs/1704.02088](https://arxiv.org/abs/1704.02088) **Localizing Moments in Video with Natural Language** - intro: ICCV 2017 - arxiv: [https://arxiv.org/abs/1708.01641](https://arxiv.org/abs/1708.01641) **Dress like a Star: Retrieving Fashion Products from Videos** - intro: Aston University - arxiv: [https://arxiv.org/abs/1710.07198](https://arxiv.org/abs/1710.07198) **Deep Hashing with Category Mask for Fast Video Retrieval** [https://arxiv.org/abs/1712.08315](https://arxiv.org/abs/1712.08315) **Focus: Querying Large Video Datasets with Low Latency and Low Cost** [https://arxiv.org/abs/1801.03493](https://arxiv.org/abs/1801.03493) **Text-to-Clip Video Retrieval with Early Fusion and Re-Captioning** - intro: Boston University, University of British Columbia - arxiv: [https://arxiv.org/abs/1804.05113](https://arxiv.org/abs/1804.05113) # Learning to Rank **Simple to Complex Cross-modal Learning to Rank** - intro: Xi’an Jiaotong University & University of Technology Sydney & National University of Singapore & CMU - arxiv: [https://arxiv.org/abs/1702.01229](https://arxiv.org/abs/1702.01229) # Deep Metric Learning **Deep metric learning using Triplet network** - arxiv: [https://arxiv.org/abs/1412.6622](https://arxiv.org/abs/1412.6622) - slides: [http://tce.technion.ac.il/wp-content/uploads/sites/8/2016/01/Elad-Hofer.pdf](http://tce.technion.ac.il/wp-content/uploads/sites/8/2016/01/Elad-Hofer.pdf) - github: [https://github.com/eladhoffer/TripletNet](https://github.com/eladhoffer/TripletNet) **Improved Deep Metric Learning with Multi-class N-pair Loss Objective** - intro: NIPS 2016 - arxiv: [http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf](http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf) **Metric Learning with Adaptive Density Discrimination** - intro: ICLR 2016. Facebook AI Research & UC Berkeley - arxiv: [https://arxiv.org/abs/1511.05939](https://arxiv.org/abs/1511.05939) - github: [https://github.com/pumpikano/tf-magnet-loss](https://github.com/pumpikano/tf-magnet-loss) - github: [https://github.com/vithursant/MagnetLoss-PyTorch/](https://github.com/vithursant/MagnetLoss-PyTorch/) **Hard-Aware Deeply Cascaded Embedding** - intro: ICCV 2017 - arxiv: [https://arxiv.org/abs/1611.05720](https://arxiv.org/abs/1611.05720) - paper: [http://openaccess.thecvf.com/content_ICCV_2017/papers/Yuan_Hard-Aware_Deeply_Cascaded_ICCV_2017_paper.pdf](http://openaccess.thecvf.com/content_ICCV_2017/papers/Yuan_Hard-Aware_Deeply_Cascaded_ICCV_2017_paper.pdf) - github: [https://github.com/PkuRainBow/Hard-Aware-Deeply-Cascaded-Embedding_release](https://github.com/PkuRainBow/Hard-Aware-Deeply-Cascaded-Embedding_release) - github: [https://github.com/PkuRainBow/Hard-Aware-Deeply-Cascaed-Embedding](https://github.com/PkuRainBow/Hard-Aware-Deeply-Cascaed-Embedding) **Learnable Structured Clustering Framework for Deep Metric Learning** - arxiv: [https://arxiv.org/abs/1612.01213](https://arxiv.org/abs/1612.01213) **Deep Metric Learning via Lifted Structured Feature Embedding** - intro: CVPR 2016 - project page(code+data): [http://cvgl.stanford.edu/projects/lifted_struct/](http://cvgl.stanford.edu/projects/lifted_struct/) - paper: [http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Song_Deep_Metric_Learning_CVPR_2016_paper.pdf](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Song_Deep_Metric_Learning_CVPR_2016_paper.pdf) - paper: [http://cvgl.stanford.edu/papers/song_cvpr16.pdf](http://cvgl.stanford.edu/papers/song_cvpr16.pdf) - github: [https://github.com/rksltnl/Deep-Metric-Learning-CVPR16](https://github.com/rksltnl/Deep-Metric-Learning-CVPR16) - github: [https://github.com/rksltnl/Caffe-Deep-Metric-Learning-CVPR16](https://github.com/rksltnl/Caffe-Deep-Metric-Learning-CVPR16) - dataset: [ftp://cs.stanford.edu/cs/cvgl/Stanford_Online_Products.zip](ftp://cs.stanford.edu/cs/cvgl/Stanford_Online_Products.zip) **Cross-modal Deep Metric Learning with Multi-task Regularization** - intro: ICME 2017 - arxiv: [https://arxiv.org/abs/1703.07026](https://arxiv.org/abs/1703.07026) **Smart Mining for Deep Metric Learning** [https://arxiv.org/abs/1704.01285](https://arxiv.org/abs/1704.01285) **DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer** - intro: TuSimple - keywords: pedestrian re-identification - arxiv: [https://arxiv.org/abs/1707.01220](https://arxiv.org/abs/1707.01220) **Deep Metric Learning with Angular Loss** - intro: ICCV 2017 - arxiv: [https://arxiv.org/abs/1708.01682](https://arxiv.org/abs/1708.01682) **Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly** [https://arxiv.org/abs/1801.04815](https://arxiv.org/abs/1801.04815) **Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval** [https://arxiv.org/abs/1802.09662](https://arxiv.org/abs/1802.09662) **Generalization in Metric Learning: Should the Embedding Layer be the Embedding Layer?** - intro: Georgia Tech - keywords: Cars-196, CUB-200-2011 and Stanford Online Product - arxiv: [https://arxiv.org/abs/1803.03310](https://arxiv.org/abs/1803.03310) **Deep Metric Learning** - github(PyTorch): [https://github.com/bnulihaixia/Deep_metric](https://github.com/bnulihaixia/Deep_metric) **Attention-based Ensemble for Deep Metric Learning** [https://arxiv.org/abs/1804.00382](https://arxiv.org/abs/1804.00382) **Online Deep Metric Learning** [https://arxiv.org/abs/1805.05510](https://arxiv.org/abs/1805.05510) **Deep Randomized Ensembles for Metric Learning** - arxiv: [https://arxiv.org/abs/1808.04469](https://arxiv.org/abs/1808.04469) - github: [https://github.com/littleredxh/DREML](https://github.com/littleredxh/DREML) **Deep Metric Learning with Hierarchical Triplet Loss** - intro: ECCV 2018 - arxiv: [https://arxiv.org/abs/1810.06951](https://arxiv.org/abs/1810.06951) **Ranked List Loss for Deep Metric Learning** - intro: CVPR 2019 - arxiv: [https://arxiv.org/abs/1903.03238](https://arxiv.org/abs/1903.03238) **Hardness-Aware Deep Metric Learning** - intro: CVPR 2019 Oral - arxiv: [https://arxiv.org/abs/1903.05503](https://arxiv.org/abs/1903.05503) - github(official, Tensorflow): [https://github.com/wzzheng/HDML](https://github.com/wzzheng/HDML) **Signal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning** - intro: CVPR 2019 - arxiv: [https://arxiv.org/abs/1904.02616](https://arxiv.org/abs/1904.02616) **Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning** - arxiv: [https://arxiv.org/abs/1904.06627](https://arxiv.org/abs/1904.06627) - github: [https://github.com/MalongTech/research-ms-loss](https://github.com/MalongTech/research-ms-loss) **Deep Metric Learning Beyond Binary Supervision** - intro: CVPR 2019 oral - arxiv: [https://arxiv.org/abs/1904.09626](https://arxiv.org/abs/1904.09626) **SoftTriple Loss: Deep Metric Learning Without Triplet Sampling** - intro: ICCV 2019 - arxiv: [https://arxiv.org/abs/1909.05235](https://arxiv.org/abs/1909.05235) **The Group Loss for Deep Metric Learning** [https://arxiv.org/abs/1912.00385](https://arxiv.org/abs/1912.00385) # Talks / Slides **TiefVision: end-to-end image similarity search engine** - intro: It covers image classification, image location ( OverFeat ) and image similarity ( Deep Ranking). - slides: [https://docs.google.com/presentation/d/16hrXJhOzkbmla9AL7JCreCuBsa5L80gm71Pfrjo7F9Y/edit#slide=id.p](https://docs.google.com/presentation/d/16hrXJhOzkbmla9AL7JCreCuBsa5L80gm71Pfrjo7F9Y/edit#slide=id.p) - github: [https://github.com/paucarre/tiefvision](https://github.com/paucarre/tiefvision) # Projects **图像检索：CNN卷积神经网络与实战** **CNN for Image Retrieval** - blog: [http://yongyuan.name/blog/CBIR-CNN-and-practice.html](http://yongyuan.name/blog/CBIR-CNN-and-practice.html) - github: [https://github.com/willard-yuan/CNN-for-Image-Retrieval](https://github.com/willard-yuan/CNN-for-Image-Retrieval) - demo: [http://yongyuan.name/pic/](http://yongyuan.name/pic/) **Visual Search Server** ![](https://raw.githubusercontent.com/AKSHAYUBHAT/VisualSearchServer/master/appcode/static/alpha3.png) - intro: A simple implementation of Visual Search using features extracted from Tensorflow inception model and Approximate Nearest Neighbors - github: [https://github.com/AKSHAYUBHAT/VisualSearchServer](https://github.com/AKSHAYUBHAT/VisualSearchServer) **Vehicle Retrieval: vehicle image retrieval using k CNNs ensemble method** - intro: ranked 1st and won the special prize in the final of the 3rd National Gradute Contest on Smart-CIty Technology and Creative Design, China - project page: [https://www.pkuml.org/resources/pku-vehicleid.html](https://www.pkuml.org/resources/pku-vehicleid.html) - github: [https://github.com/iamhankai/vehicle-retrieval-kCNNs](https://github.com/iamhankai/vehicle-retrieval-kCNNs) **A visual search engine based on Elasticsearch and Tensorflow** - keywords: faster r-cnn - github: [https://github.com/tuan3w/visual_search](https://github.com/tuan3w/visual_search) **Siamese and triplet networks with online pair/triplet mining in PyTorch** [https://github.com/adambielski/siamese-triplet](https://github.com/adambielski/siamese-triplet) **Triplet Loss and Online Triplet Mining in TensorFlow** - blog: [https://omoindrot.github.io/triplet-loss](https://omoindrot.github.io/triplet-loss) - gtihub: [https://github.com/omoindrot/tensorflow-triplet-loss](https://github.com/omoindrot/tensorflow-triplet-loss) # Blogs **Where can I buy a chair like that? – This app will tell you** ![](http://www.news.cornell.edu/sites/chronicle.cornell/files/GrokStyleApp.png?itok=3jd_S2R7) - blog: [http://www.news.cornell.edu/stories/2016/08/where-can-i-buy-chair-app-will-tell-you](http://www.news.cornell.edu/stories/2016/08/where-can-i-buy-chair-app-will-tell-you) **Using Sketches to Search for Products Online** ![](http://sketchx.eecs.qmul.ac.uk/wp-content/uploads/sites/27/2016/04/slider_template_cvpr4-1.jpg) - homepage: [http://sketchx.eecs.qmul.ac.uk/](http://sketchx.eecs.qmul.ac.uk/) - blog: [https://news.developer.nvidia.com/using-sketches-to-search-for-products-online/](https://news.developer.nvidia.com/using-sketches-to-search-for-products-online/) # Tutorials **Deep Image Retrieval: Learning global representations for image search** - youtube: [https://www.youtube.com/watch?v=yT52xDML6ys](https://www.youtube.com/watch?v=yT52xDML6ys) **Image Instance Retrieval: Overview of state-of-the-art** - youtube: [https://www.youtube.com/watch?v=EYq-rpaZn1o](https://www.youtube.com/watch?v=EYq-rpaZn1o)

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