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Learning Robust Hash Codes for Multiple Instance Image Retrieval
[article]
2017
arXiv
pre-print
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In this paper, for the first time, we introduce a multiple instance (MI) deep hashing technique for learning discriminative hash codes with weak bag-level supervision suited for large-scale retrieval. ...
For better trainability and retrieval quality, we propose a two-pronged approach that includes robust optimization and training with an auxiliary single instance hashing arm which is down-regulated gradually ...
Conclusion In this paper, for the first time, we proposed an end-to-end deep robust hashing framework, termed RMIH, for retrieval under a multiple instance setting. ...
arXiv:1703.05724v1
fatcat:kzak3jraubd33lpsosuteomdpi
Region-DH: Region-based Deep Hashing for Multi-Instance Aware Image Retrieval
2020
2020 International Joint Conference on Neural Networks (IJCNN)
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This paper introduces an instance-aware hashing approach Region-DH for large-scale multi-label image retrieval. ...
We design a unified deep neural network that simultaneously localizes and recognizes objects while learning the hash functions for binary codes. ...
The instance similarity is simultaneously used for feature learning and hash coding. ...
doi:10.1109/ijcnn48605.2020.9207485
dblp:conf/ijcnn/MtopeW20
fatcat:zvyrgns65vfabgyui6sy2rxiwa
Self-supervised asymmetric deep hashing with margin-scalable constraint
[article]
2021
arXiv
pre-print
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However, it is still challenging to produce compact and discriminative hash codes for images associated with multiple semantics for two main reasons, 1) similarity constraints designed in most of the existing ...
By further exploiting semantic dictionaries, a new margin-scalable constraint is employed for both precise similarity searching and robust hash code generation. ...
Program for Chongqing Overseas Returnees (CX2018075). ...
arXiv:2012.03820v3
fatcat:fscm4ggdyrct3o6kso53mmriou
A Decade Survey of Content Based Image Retrieval using Deep Learning
[article]
2020
arXiv
pre-print
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This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. ...
Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. ...
In 2020, Chen et al. have proposed a deep multiple-instance ranking based hashing (DMIRH) model for multi-label image retrieval by employing the category-aware bag of feature [212] . ...
arXiv:2012.00641v1
fatcat:2zcho2szpzcc3cs6uou3jpcley
Efficient Visual Recognition
2020
International Journal of Computer Vision
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binary codes for personal-
ized image retrieval
Personalized image retrieval
A general framework for deep supervised discrete
hashing
Image retrieval
Product quantization network for fast visual ...
search
Image and video retrieval
Weakly-supervised semantic guided hashing for
social image retrieval
Social image retrieval
Anchor-based self-ensembling for semisupervised
deep pairwise hashing ...
doi:10.1007/s11263-020-01351-w
fatcat:mbcq6shmerbo5njayscgb3t4rq
Hierarchical semantic interaction-based deep hashing network for cross-modal retrieval
2021
PeerJ Computer Science
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for high-performance cross-modal hashing retrieval. ...
Due to the high efficiency of hashing technology and the high abstraction of deep networks, deep hashing has achieved appealing effectiveness and efficiency for large-scale cross-modal retrieval. ...
Dual-similarity measurement For most cross-modal retrieval benchmark datasets, it is common for an image or text to have multiple labels. ...
doi:10.7717/peerj-cs.552
pmid:34141884
pmcid:PMC8176532
fatcat:7m3vtgxggzgwxemjr25nxgqnxy
Discriminative Supervised Hashing for Cross-Modal similarity Search
[article]
2019
arXiv
pre-print
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Hashing (DSH), to learn the discrimative unified binary codes for multi-modal data. ...
As multiple modal data reflect similar semantic content, many researches aim at learning unified binary codes. However, discriminative hashing features learned by these methods are not adequate. ...
Generating Hash Codes For an unseen sample, we can generate a binary code by the learned hash function. ...
arXiv:1812.07660v3
fatcat:mijrrvmasngujk4hd2j67upuby
Hashing with Binary Matrix Pursuit
[chapter]
2018
Lecture Notes in Computer Science
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and more robust codes. ...
Incorporating our findings, we propose a novel two-stage hashing method that significantly outperforms previous hashing studies on widely used image retrieval benchmarks. ...
The authors thank Sarah Adel Bargal for helpful discussions. This work is primarily conducted at Boston University, supported in part by a BU IGNITION award, and equipment donated by NVIDIA. ...
doi:10.1007/978-3-030-01228-1_21
fatcat:mhaeamzhaneq3lywkpgl5dawvq
Anchor Graph Structure Fusion Hashing for Cross-Modal Similarity Search
[article]
2022
arXiv
pre-print
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Furthermore, a discrete optimization framework is designed to learn the unified binary codes. Extensive experimental results on three public social datasets demonstrate the superiority of AGSFH. ...
existing CMH methods relax the discrete constraints to solve the optimization objective, significantly degrading the retrieval performance. ...
Cross View Hashing (CVH) extends the single-modal spectral hashing to multiple modalities and relaxes the minimization problem for learning the hash codes [21] . ...
arXiv:2202.04327v1
fatcat:oxdytmhxyzavnetqbobl4dueka
Hashing with Binary Matrix Pursuit
[article]
2018
arXiv
pre-print
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and more robust codes. ...
Incorporating our findings, we propose a novel two-stage hashing method that significantly outperforms previous hashing studies on widely used image retrieval benchmarks. ...
The authors thank Sarah Adel Bargal for helpful discussions. This work is primarily conducted at Boston University, supported in part by a BU IGNITION award, and equipment donated by NVIDIA. ...
arXiv:1808.01990v1
fatcat:lpembrsfqjhf7kci5sihy23usu
Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval
2018
IEEE Transactions on Image Processing
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In order to achieve efficient similarity searching, hash functions are designed to encode images into lowdimensional binary codes with the constraint that similar features will have a short distance in ...
However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. ...
By incorporating these criteria into our unified framework, the resultant hash code is more robust for largescale retrieval tasks on different benchmarks. ...
doi:10.1109/tip.2017.2781422
pmid:29324416
fatcat:ks6cfierdvcrreq5w644pql3ou
Supervised Short-Length Hashing
2019
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
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However, when retrieving using an extremely short length hash code learned by the existing methods, the performance cannot be guaranteed because of severe information loss. ...
To the best of our knowledge, this is the first linear-based hashing method that focuses on both short and long-length hash codes for maintaining high precision. ...
Natural Science Foundation of China (61876098, 61671274, 61573219), China Postdoctoral Science Foundation (2016M592190), Shandong Provincial Key Research and Development Plan (2017CXGC1504), Special funds for ...
doi:10.24963/ijcai.2019/420
dblp:conf/ijcai/LiuNZX0Y19
fatcat:23nemx2zabcwzgcydin3ugxqay
Deep Multi-View Enhancement Hashing for Image Retrieval
[article]
2020
arXiv
pre-print
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This is a completely new hash learning method that combines multi-view and deep learning methods. ...
However, large-scale high-speed retrieval through binary code has a certain degree of reduction in retrieval accuracy compared to traditional retrieval methods. ...
In this work, a supervised multi-view hashing method called D-MVE-Hash is proposed for accurate and efficient image retrieval based on multiple visual features, multiview hash and deep learning. ...
arXiv:2002.00169v2
fatcat:rdmr3p6fqjct3joywouap3dgmu
Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval
[article]
2019
arXiv
pre-print
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Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. ...
In this paper, a new deep hashing method is proposed for multi-label image retrieval by re-defining the pairwise similarity into an instance similarity, where the instance similarity is quantified into ...
[43] proposes an online multiple kernel learning method, which aims to find the optimal combination of multiple kernels for similarity learning, and [44] improves the online multi-kernel learning ...
arXiv:1803.02987v3
fatcat:koevkgmdtjgxpomsqwcaygi2zy
Latent semantic sparse hashing for cross-modal similarity search
2014
Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR '14
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Similarity search methods based on hashing for effective and efficient cross-modal retrieval on large-scale multimedia databases with massive text and images have attracted considerable attention. ...
In particular, LSSH uses Sparse Coding to capture the salient structures of images, and Matrix Factorization to learn the latent concepts from text. ...
Up) Existing CMH methods learn independent hash codes for each modal of instances. ...
doi:10.1145/2600428.2609610
dblp:conf/sigir/ZhouDG14
fatcat:7mkvr5ijyvfxlcc2aryrhtefzm
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