Fetching the paper…
Reading the bibliography…
Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining.
An algorithm for finding best matches in logarithmic expected time
Jerome H Friedman, Jon Louis Bentley, and Raphael Ari Finkel. 1977 · 1977
Earlier work this paper cites.
On the resemblance and containment of documents. In Proceedings of the Compression and Complexity of Sequence . 21–29
Andrei Z Broder. 1997 · 1997
Earlier work this paper cites.
Syntactic clustering of the web
Andrei Z Broder, Steven C Glassman, Mark S Manasse, and Geoffrey Zweig. 1997 · 1997
Earlier work this paper cites.
Approximate nearest neighbors: towards removing the curse of dimensionality. In Proceedings of the Annual ACM Symposium on Theory of Computing . 604–613
Piotr Indyk and Rajeev Motwani. 1998 · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998 · 1998
Earlier work this paper cites.
Learning to hash with graph neural networks for recommender systems. In Proceedings of the Web Conference . 1988–1998
Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, and Xia Hu. 2020 · 1998
Earlier work this paper cites.
When is “nearest neighbor” meaningful?. In Proceedings of the International Conference on Database Theory . 217–235
Kevin Beyer, Jonathan Goldstein, Raghu Ramakrishnan, and Uri Shaft. 1999 · 1999
Earlier work this paper cites.
Similarity search in high dimensions via hashing. In Proceedings of the International Conference on Very Large Data Bases , Vol. 99. 518–529
Aristides Gionis, Piotr Indyk, Rajeev Motwani, et al · 1999
Earlier work this paper cites.
Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases
Christian Böhm, Stefan Berchtold, and Daniel A Keim. 2001 · 2001
Earlier work this paper cites.
Similarity estimation techniques from rounding algorithms. In Proceedings of the Annual ACM Symposium on Theory of Computing . 380–388
Moses S Charikar. 2002 · 2002
Earlier work this paper cites.
Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the Annual Symposium on Computational Geometry . 253–262
Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S Mirrokni. 2004 · 2004
Earlier work this paper cites.
Fast hash table lookup using extended bloom filter: an aid to network processing
Haoyu Song, Sarang Dharmapurikar, Jonathan Turner, and John Lockwood. 2005 · 2005
Earlier work this paper cites.
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In Proceedings of the Annual IEEE Symposium on Foundations of Computer Science . 459–468
Alexandr Andoni and Piotr Indyk. 2006 · 2006
Earlier work this paper cites.
A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. 2006 · 2006
Earlier work this paper cites.
Lower bounds on locality sensitive hashing. In Proceedings of the Annual Symposium on Computational Geometry . 154–157
Rajeev Motwani, Assaf Naor, and Rina Panigrahi. 2006 · 2006
Earlier work this paper cites.
Multi-probe LSH: efficient indexing for high-dimensional similarity search. In Proceedings of the International Conference on Very Large Data Bases . 950–961
Qin Lv, William Josephson, Zhe Wang, Moses Charikar, and Kai Li. 2007 · 2007
Earlier work this paper cites.
NUS-WIDE: a real-world web image database from National University of Singapore. In Proceedings of the ACM International Conference on Image and Video Retrieval . 48
Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. 2009 · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 248–255
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009 · 2009
Earlier work this paper cites.
Natural image statistics: A probabilistic approach to early computational vision. Vol. 39
Aapo Hyvärinen, Jarmo Hurri, and Patrick O Hoyer. 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Learning to hash with binary reconstructive embeddings. In Proceedings of the Conference on Neural Information Processing Systems . 1042–1050
Brian Kulis and Trevor Darrell. 2009 · 2009
Earlier work this paper cites.
Fast approximate nearest neighbors with automatic algorithm configuration
Marius Muja and David G Lowe. 2009 · 2009
Earlier work this paper cites.
Semantic hashing
Ruslan Salakhutdinov and Geoffrey Hinton. 2009 · 2009
Earlier work this paper cites.
Spectral hashing. In Proceedings of the Conference on Neural Information Processing Systems . 1753–1760
Yair Weiss, Antonio Torralba, and Rob Fergus. 2009 · 2009
Earlier work this paper cites.
Scalable similarity search with optimized kernel hashing. In Proceedings of the International ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 1129–1138
Junfeng He, Wei Liu, and Shih-Fu Chang. 2010 · 2010
Earlier work this paper cites.
Product quantization for nearest neighbor search
Herve Jegou, Matthijs Douze, and Cordelia Schmid. 2010 · 2010
Earlier work this paper cites.
Deep Unsupervised Image Hashing by Maximizing Bit Entropy. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 2002–2010
Yunqiang Li and Jan van Gemert. 2021 · 2010
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al · 2011
Earlier work this paper cites.
Fast locality-sensitive hashing. In Proceedings of the International ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 1073–1081
Anirban Dasgupta, Ravi Kumar, and Tamás Sarlós. 2011 · 2011
Earlier work this paper cites.
Fast approximate nearest-neighbor search with k-nearest neighbor graph. In Proceedings of the International Joint Conference on Artificial Intelligence
Kiana Hajebi, Yasin Abbasi-Yadkori, Hossein Shahbazi, and Hong Zhang. 2011 · 2011
Earlier work this paper cites.
Hashing with graphs. In Proceedings of the International Conference on Machine Learning
Wei Liu, Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. 2011 · 2011
Earlier work this paper cites.
Sparse autoencoder
Andrew Ng et al · 2011
Earlier work this paper cites.
LDAHash: Improved matching with smaller descriptors
Christoph Strecha, Alex Bronstein, Michael Bronstein, and Pascal Fua. 2011 · 2011
Earlier work this paper cites.
Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval
Yunchao Gong, Svetlana Lazebnik, Albert Gordo, and Florent Perronnin. 2012b · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks. In Proceedings of the Conference on Neural Information Processing Systems . 1097–1105
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Hamming distance metric learning. In Proceedings of the Conference on Neural Information Processing Systems . 1061–1069
Mohammad Norouzi, David J Fleet, and Russ R Salakhutdinov. 2012 · 2012
Earlier work this paper cites.
Unsupervised discovery of mid-level discriminative patches. In Proceedings of the European Conference on Computer Vision. 73–86
Saurabh Singh, Abhinav Gupta, and Alexei A Efros. 2012 · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013 · 2013
Earlier work this paper cites.
Optimized product quantization
Tiezheng Ge, Kaiming He, Qifa Ke, and Jian Sun. 2013 · 2013
Earlier work this paper cites.
Explaining adaboost
Robert E Schapire. 2013 · 2013
Earlier work this paper cites.
Return of the devil in the details: Delving deep into convolutional nets. In Proceedings of the British Machine Vision Conference
Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Generative adversarial nets. In Proceedings of the Conference on Neural Information Processing Systems . 2672–2680
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Query-adaptive hash code ranking for fast nearest neighbor search. In Proceedings of the ACM International Conference on Multimedia . 1005–1008
Tianxu Ji, Xianglong Liu, Cheng Deng, Lei Huang, and Bo Lang. 2014 · 2014
Earlier work this paper cites.
Locally optimized product quantization for approximate nearest neighbor search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2321–2328
Yannis Kalantidis and Yannis Avrithis. 2014 · 2014
Earlier work this paper cites.
Discrete graph hashing. In Proceedings of the Conference on Neural Information Processing Systems , Vol. 27
Wei Liu, Cun Mu, Sanjiv Kumar, and Shih-Fu Chang. 2014 · 2014
Earlier work this paper cites.
Approximate nearest neighbor algorithm based on navigable small world graphs
Yury Malkov, Alexander Ponomarenko, Andrey Logvinov, and Vladimir Krylov. 2014 · 2014
Earlier work this paper cites.
Optimal lower bounds for locality-sensitive hashing (except when q is tiny)
Ryan O’Donnell, Yi Wu, and Yuan Zhou. 2014 · 2014
Earlier work this paper cites.
Hashing for similarity search: A survey
Jingdong Wang, Heng Tao Shen, Jingkuan Song, and Jianqiu Ji. 2014 · 2014
Earlier work this paper cites.
Supervised hashing for image retrieval via image representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence
Rongkai Xia, Yan Pan, Hanjiang Lai, Cong Liu, and Shuicheng Yan. 2014 · 2014
Earlier work this paper cites.
Composite Quantization for Approximate Nearest Neighbor Search.. In Proceedings of the IEEE International Conference on Multimedia and Expo , Vol. 2. 3
Ting Zhang, Chao Du, and Jingdong Wang. 2014 · 2014
Earlier work this paper cites.
Hashing with binary autoencoders. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 557–566
Miguel A Carreira-Perpinán and Ramin Raziperchikolaei. 2015 · 2015
Earlier work this paper cites.
Deep hashing for compact binary codes learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2475–2483
Venice Erin Liong, Jiwen Lu, Gang Wang, Pierre Moulin, and Jie Zhou. 2015 · 2015
Earlier work this paper cites.
Simultaneous feature learning and hash coding with deep neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 3270–3278
Hanjiang Lai, Yan Pan, Ye Liu, and Shuicheng Yan. 2015 · 2015
Earlier work this paper cites.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
Earlier work this paper cites.
Deep learning of binary hash codes for fast image retrieval. In Proceedings of the IEEE Conference on Computer vision and Pattern Recognition Workshops . 27–35
Kevin Lin, Huei-Fang Yang, Jen-Hao Hsiao, and Chu-Song Chen. 2015 · 2015
Earlier work this paper cites.
Supervised discrete hashing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 37–45
Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen. 2015 · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition. In Proceedings of the International Conference on Learning Representations
Karen Simonyan and Andrew Zisserman. 2015 · 2015
Earlier work this paper cites.
Learning to hash for indexing big data—A survey
Jun Wang, Wei Liu, Sanjiv Kumar, and Shih-Fu Chang. 2015 · 2015
Earlier work this paper cites.
Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification
Ruimao Zhang, Liang Lin, Rui Zhang, Wangmeng Zuo, and Lei Zhang. 2015 · 2015
Cited alongside, same era.
Deep semantic ranking based hashing for multi-label image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1556–1564
Fang Zhao, Yongzhen Huang, Liang Wang, and Tieniu Tan. 2015 · 2015
Cited alongside, same era.
Deep quantization network for efficient image retrieval. In Proceedings of the AAAI Conference on Artificial Intelligence
Yue Cao, Mingsheng Long, Jianmin Wang, Han Zhu, and Qingfu Wen. 2016 · 2016
Cited alongside, same era.
Cross-modal hashing via rank-order preserving
Kun Ding, Bin Fan, Chunlei Huo, Shiming Xiang, and Chunhong Pan. 2016 · 2016
Cited alongside, same era.
Learning to hash with binary deep neural network. In Proceedings of the European Conference on Computer Vision. 219–234
Thanh-Toan Do, Anh-Dzung Doan, and Ngai-Man Cheung. 2016 · 2016
Hadamard Codebook Based Deep Hashing
Shen Chen, Liujuan Cao, Mingbao Lin, Yan Wang, Xiaoshuai Sun, Chenglin Wu, Jingfei Qiu, and Rongrong Ji. 2019a · 2019
Later among the works it cites.
Deep Discrete Hashing with Pairwise Correlation Learning
Yaxiong Chen and Xiaoqiang Lu. 2019 · 2019
Later among the works it cites.
Deep spherical quantization for image search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 11690–11699
Sepehr Eghbali and Ladan Tahvildari. 2019 · 2019
Later among the works it cites.
Neurons merging layer: towards progressive redundancy reduction for deep supervised hashing. In Proceedings of the International Joint Conference on Artificial Intelligence . 2322–2328
Chaoyou Fu, Liangchen Song, Xiang Wu, Guoli Wang, and Ran He. 2019 · 2019
Later among the works it cites.
Clustering-driven unsupervised deep hashing for image retrieval
Yifan Gu, Shidong Wang, Haofeng Zhang, Yazhou Yao, Wankou Yang, and Li Liu. 2019b · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep residual learning for image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Cited alongside, same era.
Unsupervised learning of discriminative attributes and visual representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 5175–5184
Chen Huang, Chen Change Loy, and Xiaoou Tang. 2016 · 2016
Cited alongside, same era.
Learning compact binary descriptors with unsupervised deep neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1183–1192
Kevin Lin, Jiwen Lu, Chu-Song Chen, and Jie Zhou. 2016 · 2016
Cited alongside, same era.
Query-adaptive hash code ranking for large-scale multi-view visual search
Xianglong Liu, Lei Huang, Cheng Deng, Bo Lang, and Dacheng Tao. 2016a · 2016
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller. 2016 · 2016
Cited alongside, same era.
A fast optimization method for general binary code learning
Fumin Shen, Xiang Zhou, Yang Yang, Jingkuan Song, Heng Tao Shen, and Dacheng Tao. 2016 · 2016
Cited alongside, same era.
Deep supervised hashing with triplet labels. In Proceedings of the Asian Conference on Computer Vision . 70–84
Xiaofang Wang, Yi Shi, and Kris M Kitani. 2016 · 2016
Cited alongside, same era.
Later among the works it cites.
Towards a deep and unified understanding of deep neural models in nlp. In Proceedings of the International Conference on Machine Learning . 2454–2463
Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, and Xing Xie. 2019 · 2019
Later among the works it cites.
One network for multi-domains: domain adaptive hashing with intersectant generative adversarial networks. In Proceedings of the International Joint Conference on Artificial Intelligence . 2477–2483
Tao He, Yuan-Fang Li, Lianli Gao, Dongxiang Zhang, and Jingkuan Song. 2019 · 2019
Later among the works it cites.
Accelerate Learning of Deep Hashing With Gradient Attention. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 5271–5280
Long-Kai Huang, Jianda Chen, and Sinno Jialin Pan. 2019 · 2019
Later among the works it cites.
Deep multi-level semantic hashing for cross-modal retrieval
Zhenyan Ji, Weina Yao, Wei Wei, Houbing Song, and Huaiyu Pi. 2019 · 2019
Later among the works it cites.
Maximum-Margin Hamming Hashing. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 8252–8261
Rong Kang, Yue Cao, Mingsheng Long, Jianmin Wang, and Philip S Yu. 2019 · 2019
Later among the works it cites.
End-to-end supervised product quantization for image search and retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 5041–5050
Benjamin Klein and Lior Wolf. 2019 · 2019
Later among the works it cites.
Weighted multi-deep ranking supervised hashing for efficient image retrieval
Jiayong Li, Wing WY Ng, Xing Tian, Sam Kwong, and Hui Wang. 2019 · 2019
Later among the works it cites.
Mutual Linear Regression-based Discrete Hashing
Xingbo Liu, Xiushan Nie, and Yilong Yin. 2019 · 2019
Later among the works it cites.
Unsupervised binary representation learning with deep variational networks
Yuming Shen, Li Liu, and Ling Shao. 2019a · 2019
Later among the works it cites.
Energy and Policy Considerations for Deep Learning in NLP. In Proceedings of the Annual Meeting of the Association for Computational Linguistics . 3645–3650
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
Later among the works it cites.
Deep incremental hashing network for efficient image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9069–9077
Dayan Wu, Qi Dai, Jing Liu, Bo Li, and Weiping Wang. 2019 · 2019
Later among the works it cites.
Asymmetric deep semantic quantization for image retrieval
Zhan Yang, Osolo Ian Raymond, Wuqing Sun, and Jun Long. 2019b · 2019
Later among the works it cites.
Deep attention-guided hashing
Zhan Yang, Osolo Ian Raymond, Wuqing Sun, and Jun Long. 2019c · 2019
Later among the works it cites.
Discrete robust supervised hashing for cross-modal retrieval
Tao Yao, Zhiwang Zhang, Lianshan Yan, Jun Yue, and Qi Tian. 2019 · 2019
Later among the works it cites.
Optimal projection guided transfer hashing for image retrieval
Lei Zhang, Ji Liu, Yang Yang, Fuxiang Huang, Feiping Nie, and David Zhang. 2019a · 2019
Later among the works it cites.
Deep supervised hashing using symmetric relative entropy
Xueni Zhang, Lei Zhou, Xiao Bai, Xiushu Luan, Jie Luo, and Edwin R Hancock. 2019b · 2019
Later among the works it cites.
Angular Deep Supervised Hashing for Image Retrieval
Chang Zhou, Lai-Man Po, Wilson YF Yuen, Kwok Wai Cheung, Xuyuan Xu, Kin Wai Lau, Yuzhi Zhao, Mengyang Liu, and Peter HW Wong. 2019 · 2019
Later among the works it cites.
A Review of Hashing Methods for Multimodal Retrieval
Wenming Cao, Wenshuo Feng, Qiubin Lin, Guitao Cao, and Zhihai He. 2020 · 2020
Closest in time.
Unsupervised Deep K-Means Hashing for Efficient Image Retrieval and Clustering
Xiao Dong, Li Liu, Lei Zhu, Zhiyong Cheng, and Huaxiang Zhang. 2020 · 2020
Closest in time.
Deep Polarized Network for Supervised Learning of Accurate Binary Hashing Codes. In Proceedings of the International Joint Conference on Artificial Intelligence . 825–831
Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, and Chee Seng Chan. 2020 · 2020
Closest in time.
Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 9729–9738
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020 · 2020
Closest in time.
Creating something from nothing: Unsupervised knowledge distillation for cross-modal hashing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 3123–3132
Hengtong Hu, Lingxi Xie, Richang Hong, and Qi Tian. 2020 · 2020
Closest in time.
Push for Quantization: Deep Fisher Hashing. In Proceedings of the British Machine Vision Conference
Yunqiang Li, Wenjie Pei, Jan van Gemert, et al · 2020
Closest in time.
Auto-encoding twin-bottleneck hashing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2818–2827
Yuming Shen, Jie Qin, Jiaxin Chen, Mengyang Yu, Li Liu, Fan Zhu, Fumin Shen, and Ling Shao. 2020 · 2020
Closest in time.
Anchor-based self-ensembling for semi-supervised deep pairwise hashing
Xiaoshuang Shi, Zhenhua Guo, Fuyong Xing, Yun Liang, and Lin Yang. 2020 · 2020
Closest in time.
MLS3RDUH: Deep Unsupervised Hashing via Manifold based Local Semantic Similarity Structure Reconstructing. In Proceedings of the International Joint Conference on Artificial Intelligence . 3466–3472
Rong-Cheng Tu, Xian-Ling Mao, and Wei Wei. 2020 · 2020
Closest in time.
Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. In Proceedings of the International Conference on Machine Learning
Tongzhou Wang and Phillip Isola. 2020 · 2020
Closest in time.
Learning coarse-to-fine graph neural networks for video-text retrieval
Wei Wang, Junyu Gao, Xiaoshan Yang, and Changsheng Xu. 2020 · 2020
Closest in time.
Central Similarity Quantization for Efficient Image and Video Retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Li Yuan, Tao Wang, Xiaopeng Zhang, Francis EH Tay, Zequn Jie, Wei Liu, and Jiashi Feng. 2020 · 2020
Closest in time.
Deep unsupervised hybrid-similarity hadamard hashing. In Proceedings of the ACM International Conference on Multimedia . 3274–3282
Wanqian Zhang, Dayan Wu, Yu Zhou, Bo Li, Weiping Wang, and Dan Meng. 2020 · 2020
Closest in time.
Learning sentence-to-hashtags semantic mapping for hashtag recommendation on microblogs
Riccardo Cantini, Fabrizio Marozzo, Giovanni Bruno, and Paolo Trunfio. 2021 · 2021
Closest in time.
Efficient inter-image relation graph neural network hashing for scalable image retrieval. In Proceedings of the ACM International Conference on Multimedia in Asia . 1–8
Hui Cui, Lei Zhu, and Wentao Tan. 2021 · 2021
Closest in time.
An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the International Conference on Learning Representations
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
Closest in time.
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective. In Proceedings of the Conference on Neural Information Processing Systems , Vol. 34
Jiun Tian Hoe, Kam Woh Ng, Tianyu Zhang, Chee Seng Chan, Yi-Zhe Song, and Tao Xiang. 2021 · 2021
Closest in time.
Video moment localization via deep cross-modal hashing
Yupeng Hu, Meng Liu, Xiaobin Su, Zan Gao, and Liqiang Nie. 2021 · 2021
Closest in time.
Domain Adaptation Preconceived Hashing for Unconstrained Visual Retrieval
Fuxiang Huang, Lei Zhang, and Xinbo Gao. 2021 · 2021
Closest in time.
Self-supervised Product Quantization for Deep Unsupervised Image Retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 12085–12094
Young Kyun Jang and Nam Ik Cho. 2021 · 2021
Closest in time.
Task-adaptive asymmetric deep cross-modal hashing
Fengling Li, Tong Wang, Lei Zhu, Zheng Zhang, and Xinhua Wang. 2021b · 2021
Closest in time.
Deep Self-Adaptive Hashing for Image Retrieval. In Proceedings of the ACM International Conference on Information & Knowledge Management . 1028–1037
Qinghong Lin, Xiaojun Chen, Qin Zhang, Shangxuan Tian, and Yudong Chen. 2021 · 2021
Closest in time.
Unsupervised deep multi-similarity hashing with semantic structure for image retrieval
Qibing Qin, Lei Huang, Zhiqiang Wei, Kezhen Xie, and Wenfeng Zhang. 2021 · 2021
Closest in time.
Unsupervised Hashing with Contrastive Information Bottleneck. In Proceedings of the International Joint Conference on Artificial Intelligence
Zexuan Qiu, Qinliang Su, Zijing Ou, Jianxing Yu, and Changyou Chen. 2021 · 2021
Closest in time.
Transductive semisupervised deep hashing
Weiwei Shi, Yihong Gong, Badong Chen, and Xinhong Hei. 2021 · 2021
Closest in time.
Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 7262–7272
Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. 2021 · 2021
Closest in time.
Partial-Softmax Loss based Deep Hashing. In Proceedings of the Web Conference . 2869–2878
Rong-Cheng Tu, Xian-Ling Mao, Jia-Nan Guo, Wei Wei, and Heyan Huang. 2021 · 2021
Closest in time.
Adversarial Binary Mutual Learning for Semi-Supervised Deep Hashing
Guan’an Wang, Qinghao Hu, Yang Yang, Jian Cheng, and Zeng-Guang Hou. 2021a · 2021
Closest in time.
Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion
Yang Wang. 2021 · 2021
Closest in time.
Deep Graph-neighbor Coherence Preserving Network for Unsupervised Cross-modal Hashing. In Proceedings of the AAAI Conference on Artificial Intelligence . 4626–4634
Jun Yu, Hao Zhou, Yibing Zhan, and Dacheng Tao. 2021 · 2021
Closest in time.
Probability ordinal-preserving semantic hashing for large-scale image retrieval
Zheng Zhang, Xiaofeng Zhu, Guangming Lu, and Yudong Zhang. 2021 · 2021
Closest in time.
Improve Deep Unsupervised Hashing via Structural and Intrinsic Similarity Learning
Xiao Luo, Zeyu Ma, Wei Cheng, and Minghua Deng. 2022 · 2022
Closest in time.
Learning to Hash Naturally Sorts
Yuming Shen, Jiaguo Yu, Haofeng Zhang, Philip HS Torr, and Menghan Wang. 2022 · 2022
Closest in time.
Binary hashing for approximate nearest neighbor search on big data: A survey
Yuan Cao, Heng Qi, Wenrui Zhou, Jien Kato, Keqiu Li, Xiulong Liu, and Jie Gui. 2017c · 2054
Closest in time.
Deep supervised hashing for fast image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2064–2072
Haomiao Liu, Ruiping Wang, Shiguang Shan, and Xilin Chen. 2016b · 2072
Closest in time.
Supervised hashing with kernels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2074–2081
Wei Liu, Jun Wang, Rongrong Ji, Yu-Gang Jiang, and Shih-Fu Chang. 2012 · 2081
Closest in time.