Image Retrieval

Papers

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

• • arxiv: http://arxiv.org/abs/1312.4740
  • paper: http://legacy.openreview.net/document/90fc8dad-ad02-4ddc-ab06-e7b55706869d#90fc8dad-ad02-4ddc-ab06-e7b55706869d

2.Neural Codes for Image Retrieval

• • project page: http://sites.skoltech.ru/compvision/projects/neuralcodes/
  • arxiv: http://arxiv.org/abs/1404.1777
  • github: https://github.com/arbabenko/Spoc

3.Efficient On-the-fly Category Retrieval using ConvNets and GPUs

• • arxiv: http://arxiv.org/abs/1407.4764

4.Deep Learning of Binary Hash Codes for Fast Image Retrieval

• • intro: CVPR Workshop 2015
  • intro: MNIST, CIFAR-10, Yahoo-1M
  • paper: http://www.iis.sinica.edu.tw/~kevinlin311.tw/cvprw15.pdf
  • github: https://github.com/kevinlin311tw/caffe-cvprw15

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