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Multi-Label Weighted Contrastive Cross-Modal Hashing
2023
Applied Sciences
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and supervised contrastive learning to consider diverse relationships among cross-modal instances, and (ii) how to reduce the sparsity of multi-label representation so as to improve the similarity measurement ...
This framework involves compact consistent similarity representation, a new designed multi-label similarity calculation method that efficiently reduces the sparsity of multi-label by reducing redundant ...
images H t •, θ t hash model for texts B i the binary hash code of the ith instance Problem Definition. ...
doi:10.3390/app14010093
fatcat:gxarghivxnffzelt22u6msikfa
Learning to Hash for Indexing Big Data - A Survey
[article]
2015
arXiv
pre-print
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The explosive growth in big data has attracted much attention in designing efficient indexing and search methods recently. ...
In response, Approximate Nearest Neighbor (ANN) search based on hashing techniques has become popular due to its promising performance in both efficiency and accuracy. ...
This will make efficient large-scale learning possible with limited resources, for instance on mobile devices. ...
arXiv:1509.05472v1
fatcat:haj52w3cbbgszlmalfyu2kvzde
Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance
[article]
2016
arXiv
pre-print
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In this work, we address the question of what reflectance can reveal about materials in an efficient manner. ...
We go beyond the question of recognition and labeling and ask the question: What intrinsic physical properties of the surface can be estimated using reflectance? ...
The combination of deep learning and binary compact hash codes is an interesting path that combines the efficiency of binary codes (hamming distance is fast) with the robust performance of deep learning ...
arXiv:1603.07998v2
fatcat:u3ahotknxndtvbx5eqqqjtktly
Learning Structured Ordinal Measures for Video based Face Recognition
[article]
2015
arXiv
pre-print
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This paper presents a structured ordinal measure method for video-based face recognition that simultaneously learns ordinal filters and structured ordinal features. ...
Unsupervised and supervised structures are considered for the ordinal matrix. ...
This can be viewed as an instance of maximum margin clustering (MMC) [41] . ...
arXiv:1507.02380v1
fatcat:7qlxoxyprvcpjpn2jv7e64ulsu
Set-to-Set Hashing with Applications in Visual Recognition
[article]
2019
arXiv
pre-print
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2017 pre-print arXiv:1711.00888v1 fatcat:jjorqm5bs5hcjaectda43rq5ou
This problem has obvious applications in large-scale visual retrieval and recognition, and also in applied fields beyond computer vision. ...
For the query set process, we adopt a learning-to-hash pipeline that turns the kernel representations into hash bits based on simple learners, using multiple kernel learning. ...
strategies, we design a boosting algorithm to learn binary splits for constructing hash functions. ...
arXiv:1711.00888v2
fatcat:curyj2mqtfdhhh2kg6wwsovzoi
Deep Image Set Hashing
[article]
2016
arXiv
pre-print
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Learning-based hashing is often used in large scale image retrieval as they provide a compact representation of each sample and the Hamming distance can be used to efficiently compare two samples. ...
The computed set feature is then fed into a multilayer perceptron to learn a compact binary embedding. ...
Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. ...
arXiv:1606.05381v2
fatcat:3hipj3exwfgdhj56sjbxlv7fsy
Hashing for Similarity Search: A Survey
[article]
2014
arXiv
pre-print
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We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions ...
An instance algorithm [76] uses an efficient GraphCut based block search method for inferring binary codes for large databases and trains boosted decision trees fit the binary codes. ...
[Σ] = σ, m = 1, · · · , M (117) R T R = I. (118)
Harmonious hashing Harmonious hashing [138] can be viewed as a combination of ITQ and Isotropic hashing. ...
arXiv:1408.2927v1
fatcat:reknwesjnbafvcbouyudrzp4rq
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization
[article]
2022
arXiv
pre-print
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To this end, we propose a lightweight federated recommendation framework with privacy-preserving matrix factorization, LightFR, that is able to generate high-quality binary codes by exploiting learning ...
However, previous work on FRS performs similarity search via inner product in continuous embedding space, which causes an efficiency bottleneck when the scale of items is extremely large. ...
ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their helpful comments and suggestions. ...
arXiv:2206.11743v1
fatcat:otr5aypnpvagpe4e3hq3stx5xe
PM-LSH
2020
Proceedings of the VLDB Endowment
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Existing LSH methods focus mainly on building hash bucket based indexing such that the candidate points can be retrieved quickly. ...
Third, we propose an efficient algorithm on top of the PM-tree to improve the performance of computing c-ANN queries. ...
To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. ...
doi:10.14778/3377369.3377374
fatcat:aas6ap7v5bfgfebinps3xkps3m
Towards large-scale nonparametric scene parsing of images and video
[article]
2017
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Nonparametric scene parsing is computationally demanding at test time, and requires methods for searching large collections of data that are time and memory efficient. ...
This thesis also presents two novel binary encoding algorithms for large-scale approximate nearest neighbor search: the bank of random rotations is data independent and does not require training, while ...
Multi-view reconstruction is used to obtain a 3D mesh model. ...
doi:10.14288/1.0343064
fatcat:vwo3iigvdjajvn4o2br4wv5g4e
Scalable Machine Learning for Visual Data
2017
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The unprecedented availability of visual data calls for machine learning methods that are effective and efficient for such large-scale settings. ...
The above hinder the applicability of machine learning methods for large-scale visual data. ...
Chapter 3 Circulant Binary Embedding
Introduction Embedding input data in binary spaces is becoming popular for efficient retrieval and learning on massive data sets [127, 73, 170, 71, 134] . ...
doi:10.7916/d8f47ndb
fatcat:xde635kzfrdvdhvakf432yod54
REFLECTANCE AND TEXTURE ENCODING FOR MATERIAL RECOGNITION AND SYNTHESIS Reflectance and Texture Encoding for Material Recognition and Synthesis
2017
unpublished
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These multi-layer deep learning representations provide invariance to intra-class variations for recognition. We also develop representations that capture sufficient detail for synthesis. ...
These reflectance disks encode discriminative information for efficient and accurate material recognition. ...
Each instance can have a different friction value, as shown in Table 3 .1.
DRC For binary embedding, projection to a lower dimensional subspace that preserves the similarity is a key component. ...
fatcat:64u6hmma2rbjbo2oqm4e7c6sti
Learning and Matching Binary Local Feature Descriptors
2014
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Keywords: Computer vision, machine learning, binary local feature descriptors, binary approximate nearest neighbor search. ...
Vincent Lepetit for his insightful comments, numerous advices, and for the always opened door to his office. ...
Semantic hashing [94] trains a multi-layer neural network to learn compact representative binary codes. ...
doi:10.5075/epfl-thesis-6226
fatcat:44ve374jivh4zlwa5qmwxyrn4u