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Deep Triplet Quantization [article]

Bin Liu, Yue Cao, Mingsheng Long, Jianmin Wang, Jingdong Wang
2019 arXiv   pre-print
Deep hashing establishes efficient and effective image retrieval by end-to-end learning of deep representations and hash codes from similarity data.  ...  We propose Deep Triplet Quantization (DTQ), a novel approach to learning deep quantization models from the similarity triplets.  ...  Following standard evaluation protocol as previous work [1, 3, 19, 37, 42] , the similarity information for hash function learning and for ground-truth evaluation is constructed from image labels: if  ... 
arXiv:1902.00153v1 fatcat:s4vopgkgdfhsjpdvwjkuqijfuu

Fast Training of Triplet-based Deep Binary Embedding Networks [article]

Bohan Zhuang, Guosheng Lin, Chunhua Shen, Ian Reid
2016 arXiv   pre-print
We make use of a triplet loss because this has been shown to be most effective for ranking problems.  ...  In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task.  ...  Note that these methods suffer from huge computation complexity introduced by the triplet ranking loss for hashing.  ... 
arXiv:1603.02844v2 fatcat:j7adme72nbc2znonmvqnpsj3ua

Vehicle Re-Identification: an Efficient Baseline Using Triplet Embedding [article]

Ratnesh Kumar, Edwin Weill, Farzin Aghdasi, Parthsarathy Sriram
2019 arXiv   pre-print
Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature.  ...  In this paper we provide an extensive evaluation of these losses applied to vehicle re-identification and demonstrate that using the best practices for learning embeddings outperform most of the previous  ...  Notice that query and gallery images are constrained to be from different cameras following the standard evaluation protocol.  ... 
arXiv:1901.01015v4 fatcat:5d4u4aykyzfg5gnjh3nhpygidm

Improving Deep Binary Embedding Networks by Order-aware Reweighting of Triplets [article]

Jikai Chen, Hanjiang Lai, Libing Geng, Yan Pan
2018 arXiv   pre-print
To this end, we propose an order-aware reweighting method to effectively train the triplet-based deep networks, which up-weights the important triplets and down-weights the uninformative triplets.  ...  Extensive evaluations on four benchmark datasets show that the proposed method achieves significant performance compared with the state-of-the-art baselines.  ...  Empirical evaluations on four datasets show that the proposed method achieves better performance than the state-of-the-art baselines.  ... 
arXiv:1804.06061v1 fatcat:l7k5y454crfnfl6wjnrx6aqkwe

Learning Sound Representations Using Triplet-loss

Kohki Mametani, Xavier Favory, Frederic Font
2020 Zenodo  
This allows for more e˙ective use of user-made and multi-labelled sound descriptions from online sound collections and removes the need for a taxonomy.  ...  However, these approaches come with a few drawbacks such as the complexity of building a taxonomy, the cost of sound annotation, and the diÿculty to utilize multi-labelled annotation.  ...  Then, given a query, the system ranks the data objects which are represented by information in the database.  ... 
doi:10.5281/zenodo.4091494 fatcat:ttuxpvdzhfdzlhf4c5fnoinhh4

Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network [article]

Peng Lu, Hangyu Lin, Yanwei Fu, Shaogang Gong, Yu-Gang Jiang, Xiangyang Xue
2019 arXiv   pre-print
photos, and 2400 sketch-photo pairs.  ...  triplet loss and classification loss.  ...  Furthermore, traditional ranking methods such as rank correlation [15] , and rankSVM [70] are also used on some SBIR methods.  ... 
arXiv:1811.11375v2 fatcat:rwbszpuonfappncji7e5fs37c4

Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-modal Retrieval [article]

Donghuo Zeng, Yi Yu, Keizo Oyama
2021 arXiv   pre-print
datasets and semantic information.  ...  Cross-modal retrieval aims to retrieve data in one modality by a query in another modality, which has been a very interesting research issue in the field of multimedia, information retrieval, and computer  ...  to the query label and AP is 59.94% in all rank list.  ... 
arXiv:1908.03737v3 fatcat:qgldi32rrng27gltfbefqay4rq

Learning Incremental Triplet Margin for Person Re-Identification

Yingying Zhang, Qiaoyong Zhong, Liang Ma, Di Xie, Shiliang Pu
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial.  ...  Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods.  ...  Global Hard Identity Searching To produce triplets with high quality negative pairs, we introduce a global hard identity searching method.  ... 
doi:10.1609/aaai.v33i01.33019243 fatcat:g4wqbogymrem7imahjngxgirlm

Learning Incremental Triplet Margin for Person Re-identification [article]

Yingying Zhang and Qiaoyong Zhong and Liang Ma and Di Xie and Shiliang Pu
2018 arXiv   pre-print
In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial.  ...  Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods.  ...  Global Hard Identity Searching To produce triplets with high quality negative pairs, we introduce a global hard identity searching method.  ... 
arXiv:1812.06576v1 fatcat:u6xcywi7ivgezib2iuvgdvh4zu

Fast Training of Triplet-Based Deep Binary Embedding Networks

Bohan Zhuang, Guosheng Lin, Chunhua Shen, Ian Reid
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We make use of a triplet loss because this has been shown to be most effective for ranking problems.  ...  In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task.  ...  Note that these methods suffer from huge computation complexity introduced by the triplet ranking loss for hashing.  ... 
doi:10.1109/cvpr.2016.641 dblp:conf/cvpr/ZhuangLSR16 fatcat:rusgv63odvgr5j3xk62c6f5ywq

A Triplet Non-Local Neural Network with Dual-Anchor Triplet Loss for High Resolution Remote Sensing Image Retrieval

Maoding Zhang, Qimin Cheng, Fang Luo, Lan Ye
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
And conventional methods often used fixed-size convolution kernels that only consider the local area with fixed sizes, thus largely ignoring the global information.  ...  We evaluate T-NLNN on three public high-resolution remote sensing datasets, and the experimental results suggest that T-NLNN has discriminative feature learning ability and outperforms other existing algorithms  ...  ACKNOWLEDGMENT The authors would like to thank the editors and anonymous reviewers for their valuable comments, which helped them improve this work.  ... 
doi:10.1109/jstars.2021.3058691 fatcat:n4as2q27znhg3kj5gil7kf354u

A weakly supervised adaptive triplet loss for deep metric learning [article]

Xiaonan Zhao, Huan Qi, Rui Luo, Larry Davis
2019 arXiv   pre-print
The method boosts the performance of triplet loss baseline by 10.6% on cross-domain data and out-performs the state-of-art model on all evaluation metrics.  ...  images are closer and dissimilar images are further from one another.  ...  Acknowledgement We thank Lailin Chen, Wei Xia, Imry Kissos, Patricia Gutierrez, Angels Borras, Etan Khanal for useful discussions, and Axel Vidales, Ben Barnes, Amy Essene, Chris Mills, Gabriel Blanco  ... 
arXiv:1909.12939v1 fatcat:3pxzexbuzzfj5b3hgcc233zza4

ASBERT: Siamese and Triplet network embedding for open question answering [article]

Olabanji Shonibare
2021 arXiv   pre-print
Experimental results on the WikiQA and TrecQA datasets demonstrate that our proposed approach outperforms many state-of-the-art baseline methods.  ...  A common approach to address the AS problem is to generate an embedding for each candidate sentence and query. Then, select the sentence whose vector representation is closest to the query's.  ...  Evaluation metrics The performance of all models presented in this work was evaluated using the Mean Reciprocal Rank (MRR) and Mean Average Precision (MAP).  ... 
arXiv:2104.08558v1 fatcat:hjgyhwqr5jgkzbw2jkgmhmqc3e

Age-Oriented Face Synthesis with Conditional Discriminator Pool and Adversarial Triplet Loss [article]

Haoyi Wang, Victor Sanchez, Chang-Tsun Li
2020 arXiv   pre-print
To achieve strong identity permanence capabilities, our method uses a novel Adversarial Triplet loss.  ...  This loss, which is based on the Triplet loss, adds a ranking operation to further pull the positive embedding towards the anchor embedding resulting in significantly reduced intra-class variances in the  ...  We use the cosine similarity to measure the distance of each pair of query and gallery images.  ... 
arXiv:2007.00792v2 fatcat:fzrlhbvb4jcipnslqivafuytmm

Deep Learning Triplet Ordinal Relation Preserving Binary Code for Remote Sensing Image Retrieval Task

Zhen Wang, Nannan Wu, Xiaohan Yang, Bingqi Yan, Pingping Liu
2021 Remote Sensing  
In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the  ...  cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code.  ...  Acknowledgments: The authors express their gratitude to the institutions that supported this research: Shandong University of Technology (SDUT) and Jilin University (JLU).  ... 
doi:10.3390/rs13234786 fatcat:v6sf2zuk4vaezisevwbhpxpipq
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