Image Retrieval（图像检索资料汇总）

1. Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data

paper: http://legacy.openreview.net/document/90fc8dad-ad02-4ddc-ab06-e7b55706869d#90fc8dad-ad02-4ddc-ab06-e7b55706869d

5.Learning visual similarity for product design with convolutional neural networks

intro: SIGGRAPH 2015
paper: http://www.cs.cornell.edu/~kb/publications/SIG15ProductNet.pdf
paper: http://dl.acm.org.sci-hub.cc/citation.cfm?doid=2809654.2766959

6.Deep Semantic Ranking Based Hashing for Multi-Label Image Retrieval

intro: CVPR 2015
arxiv: http://arxiv.org/abs/1501.06272

7.Exploiting Local Features from Deep Networks for Image Retrieval

intro: CVPR DeepVision Workshop 2015
arxiv: https://arxiv.org/abs/1504.05133

8.Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search

intro: SSDH
arxiv: http://arxiv.org/abs/1507.00101
github: https://github.com/kevinlin311tw/Caffe-DeepBinaryCode

9.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

10.Aggregating Deep Convolutional Features for Image Retrieval

intro: ICCV 2015
intro: Sum pooing
arxiv: http://arxiv.org/abs/1510.07493

11.Feature Learning based Deep Supervised Hashing with Pairwise Labels

intro: IJCAI 2016
arxiv: http://arxiv.org/abs/1511.03855
code: http://cs.nju.edu.cn/lwj/code/DPSH_code.rar

12.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

Group Invariant Deep Representations for Image Instance Retrieval
arxiv: http://arxiv.org/abs/1601.02093

13.Where to Buy It: Matching Street Clothing Photos in Online Shops

intro: ICCV 2015
hmepage: http://www.tamaraberg.com/street2shop/
paper: 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

14.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
arxiv: https://arxiv.org/abs/1604.01325
slides: http://www.slideshare.net/xavigiro/deep-image-retrieval-learning-global-representations-for-image-search

15.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/
arxiv: https://arxiv.org/abs/1604.04653
github: https://github.com/imatge-upc/retrieval-2016-icmr
slides: http://www.slideshare.net/xavigiro/convolutional-features-for-instance-search

16.Faster R-CNN Features for Instance Search

intro: DeepVision Workshop in CVPR 2016
homepage: http://imatge-upc.github.io/retrieval-2016-deepvision/
arxiv: http://arxiv.org/abs/1604.08893
github: https://github.com/imatge-upc/retrieval-2016-deepvision

17.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
github: https://github.com/kevinlin311tw/cvpr16-deepbit

18.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

19.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

20.CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples

intro: ECCV 2016
project page(paper+code+data): http://cmp.felk.cvut.cz/~radenfil/projects/siamac.html
arxiv: https://arxiv.org/abs/1604.02426
paper: http://cmp.felk.cvut.cz/~radenfil/publications/Radenovic-ECCV16.pdf
code(Matlab): http://ptak.felk.cvut.cz/personal/radenfil/siamac/siaMAC_code.tar.gz

21.SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval

arxiv: http://arxiv.org/abs/1607.08477

22.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

23.SIFT Meets CNN: A Decade Survey of Instance Retrieval

arxiv: http://arxiv.org/abs/1608.01807

24.Deep Hashing: A Joint Approach for Image Signature Learning

arxiv: http://arxiv.org/abs/1608.03658

25.End-to-end Learning of Deep Visual Representations for Image Retrieval

intro: ECCV 2016
project page: http://www.xrce.xerox.com/Research-Development/Computer-Vision/Learning-Visual-Representations/Deep-Image-Retrieval
arxiv: https://arxiv.org/abs/1610.07940

参考资料：
https://blog.csdn.net/TTdreamloong/article/details/79216937
https://blog.csdn.net/YiLiang_/article/details/60751264