- [Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval](https://www.robots.ox.ac.uk/~vgg/publications/papers/philbin07.pdf)
- [Three things everyone should know to improve object retrieval](https://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf)
- [On-the-fly learning for visual search of large-scale image and video datasets](https://www.robots.ox.ac.uk/~vgg/publications/2015/Chatfield15/chatfield15.pdf)
- [Aggregating Deep Convolutional Features for Image Retrieval](https://arxiv.org/abs/1510.07493), [论文笔记](https://zhuanlan.zhihu.com/p/23136747), [基于深度学习的视觉实例搜索研究进展](https://zhuanlan.zhihu.com/p/22265265).
- [Particular object retrieval with integral max-pooling of CNN activations](https://arxiv.org/abs/1511.05879), [project](http://cmp.felk.cvut.cz/~toliageo/soft.html), [Learning to Match Aerial Images with Deep Attentive Architectures](https://vision.cornell.edu/se3/wp-content/uploads/2016/04/1204.pdf).
- [Practical and Optimal LSH for Angular Distance](chrome-extension://ikhdkkncnoglghljlkmcimlnlhkeamad/pdf-viewer/web/viewer.html?file=http%3A%2F%2Fpapers.nips.cc%2Fpaper%2F5893-practical-and-optimal-lsh-for-angular-distance.pdf)
- [faiss](https://github.com/facebookresearch/faiss). A library for efficient similarity search and clustering of dense vectors.
- [lopq](https://github.com/yahoo/lopq). Training of Locally Optimized Product Quantization (LOPQ) models for approximate nearest neighbor search of high dimensional data in Python and Spark.
- [nns_benchmark](https://github.com/DBWangGroupUNSW/nns_benchmark). Benchmark of Nearest Neighbor Search on High Dimensional Data.
- [Falconn](https://github.com/FALCONN-LIB/FALCONN). FAst Lookups of Cosine and Other Nearest Neighbors.
- [Annoy](https://github.com/spotify/annoy). Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
- [NMSLIB](https://github.com/searchivarius/nmslib). Non-Metric Space Library (NMSLIB): A similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.
- [Large-scale Video Classification with Convolutional Neural Networks](vision.stanford.edu/pdf/karpathy14.pdf)
- [Learning Spatiotemporal Features With 3D Convolutional Networks](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Tran_Learning_Spatiotemporal_Features_ICCV_2015_paper.pdf), [code](https://github.com/Lasagne/Recipes/blob/master/examples/Video%20features%20with%20C3D.ipynb), [doc](https://docs.google.com/document/d/1-QqZ3JHd76JfimY4QKqOojcEaf5g3JS0lNh-FHTxLag/edit), [project](http://vlg.cs.dartmouth.edu/c3d/)