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Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives [article]

Gencer Sumbul, Jian Kang, Begüm Demir
2020 arXiv   pre-print
After discussing their strengths and limitations, we present the deep hashing based CBIR systems that have high time-efficient search capability within huge data archives.  ...  Then, we focus our attention on the advances in RS CBIR systems for which deep learning (DL) models are at the forefront.  ...  TDMLN employs three CNNs with shared model parameters for similarity learning through image triplets in the content of metric learning.  ... 
arXiv:2004.01613v2 fatcat:d4fjt3vzybbbrejxzobaluqsoq

Deep similarity learning for multimodal medical images

Xi Cheng, Li Zhang, Yefeng Zheng
2016 Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization  
Therefore, approaches of learning a similarity metric are proposed in recent years.  ...  In this work, we propose a novel deep similarity learning method that trains a binary classifier to learn the correspondence of two image patches.  ...  Besides, the similarity measure is only informative in regions with texture and edges rather than homogeneous regions.  ... 
doi:10.1080/21681163.2015.1135299 fatcat:uzc4rnc4xbdmfdrgvqbilbrdsu

Person search: New paradigm of person re-identification: A survey and outlook of recent works

Khawar Islam
2020 Image and Vision Computing  
This survey paper includes brief discussion about feature representation learning and deep metric learning with novel loss functions.  ...  In last few years, deep learning has played unremarkable role for the solution of re-identification problem. Deep learning shows incredible performance in person (re-ID) and search.  ...  Deep metric learning approach Deep metric learning is a technique to calculate the distance between two data spaces and decide whether objects are similar or dissimilar.  ... 
doi:10.1016/j.imavis.2020.103970 fatcat:g2zuqww7tbdszkxrc2wkrfno2y

A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

Yating Gu, Yantian Wang, Yansheng Li
2019 Applied Sciences  
Similar to other domains (e.g., speech recognition and natural image recognition), deep learning has also become the state-of-the-art technique in RSISU.  ...  To facilitate the sustainable progress of RSISU, this paper presents a comprehensive review of deep-learning-based RSISU methods, and points out some future research directions and potential applications  ...  To solve this problem, researchers have recently proposed the development of more suitable similarity metrics, such as deep features with hash learning, and the end-to-end deep hash learning method.  ... 
doi:10.3390/app9102110 fatcat:oj3acgbmwnhzppxvvjbsn5cfzq

Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision [article]

Bo Li, Xinyang Jiang, Donglin Bai, Yuge Zhang, Ningxin Zheng, Xuanyi Dong, Lu Liu, Yuqing Yang, Dongsheng Li
2021 arXiv   pre-print
With the progress of efficient deep learning techniques, e.g., model compression, researchers can obtain efficient models with fewer parameters and smaller latency.  ...  However, most of the existing efficient deep learning methods do not explicitly consider energy consumption as a key performance indicator.  ...  Greeness in Efficient Deep Learning In this section, we introduce a new metric -Greeness to evaluate the efficient deep learning algorithms by directly measuring their energy consumption during the entire  ... 
arXiv:2108.13465v2 fatcat:gqiu7mvyhrawdipyhm6x3j77tq

Deep Learning for Instance Retrieval: A Survey [article]

Wei Chen, Yu Liu, Weiping Wang, Erwin Bakker, Theodoros Georgiou, Paul Fieguth, Li Liu, Michael S. Lew
2022 arXiv   pre-print
In this survey we review recent instance retrieval works that are developed based on deep learning algorithms and techniques, with the survey organized by deep network architecture types, deep features  ...  This abundance of content creation and sharing has introduced new challenges, particularly that of searching databases for similar content-Content Based Image Retrieval (CBIR)-a long-established research  ...  For image retrieval, attention mechanisms can be combined with supervised metric learning [70] .  ... 
arXiv:2101.11282v3 fatcat:qvodunmw4bdltcneadyt7d7h5m

Region-DH: Region-based Deep Hashing for Multi-Instance Aware Image Retrieval

Franck Romuald Fotso Mtope, Bo Wei
2020 2020 International Joint Conference on Neural Networks (IJCNN)  
Region-DH can flexibly be used with existing deep neural networks or more complex object detectors for image hashing.  ...  We design a unified deep neural network that simultaneously localizes and recognizes objects while learning the hash functions for binary codes.  ...  Instance Similarity Deep Hashing (ISDH) [26] defines a new pair-wise similarity metric preserving hashing into an instance similarity hashing.  ... 
doi:10.1109/ijcnn48605.2020.9207485 dblp:conf/ijcnn/MtopeW20 fatcat:zvyrgns65vfabgyui6sy2rxiwa

SID4VAM: A Benchmark Dataset with Synthetic Images for Visual Attention Modeling [article]

David Berga, Xosé R. Fdez-Vidal, Xavier Otazu, Xosé M. Pardo
2019 arXiv   pre-print
Our study reveals that state-of-the-art Deep Learning saliency models do not perform well with synthetic pattern images, instead, models with Spectral/Fourier inspiration outperform others in saliency  ...  SID4VAM is composed of 230 synthetic images, with known salient regions.  ...  In particular, visual features learned with deep learning models might not be suitable for efficiently predicting saliency using psychophysical images.  ... 
arXiv:1910.13066v1 fatcat:qd7fprgz3bemzk35cke36qywse

Deep Barcodes for Fast Retrieval of Histopathology Scans [article]

Meghana Dinesh Kumar, Morteza Babaie, Hamid Tizhoosh
2018 arXiv   pre-print
Since binary search is computationally less expensive, in terms of both speed and storage, deep barcodes could be useful when dealing with big data retrieval.  ...  Apart from the high-speed and efficiency, results show a surprising retrieval accuracy of 71.62% for deep barcodes, as compared to 68.91% for deep features and 68.53% for compressed deep features.  ...  In tables 2-6, N PCA denotes the number of principle components used, along with the distance metric for similarity measure.  ... 
arXiv:1805.08833v1 fatcat:g2axmzjjyfgw5hnwfkaq3g7urq

Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization [article]

Li Ren, Kai Li, LiQiang Wang, Kien Hua
2020 arXiv   pre-print
The objective is to find efficient similarity metrics to compare the similarity between visual and textual information.  ...  Our approach can generally improve the learning efficiency and the performance of existing metrics learning frameworks by regulating the distribution of the hidden space between the matching pairs.  ...  The objective is to find efficient similarity metrics to compare the similarity between visual and textual information.  ... 
arXiv:2010.12126v2 fatcat:we74xd3jdzdzlev2fewj7spf7m

Multi-Stream Deep Similarity Learning Networks for Visual Tracking

Kunpeng Li, Yu Kong, Yun Fu
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
A multi-stream deep similarity learning network is proposed to learn the similarity comparison model.  ...  The loss function of our network encourages the distance between a positive patch in the search region and the target template to be smaller than that between positive patch and the background patches.  ...  A multistream deep neural network is proposed to learn this general similarity comparison model with a loss function minimizing a relative distance.  ... 
doi:10.24963/ijcai.2017/301 dblp:conf/ijcai/LiKF17 fatcat:pqxfbkhtwbg2nlrunfpyemckc4

Visual Search at Alibaba

Yanhao Zhang, Pan Pan, Yun Zheng, Kang Zhao, Yingya Zhang, Xiaofeng Ren, Rong Jin
2018 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD '18  
We take advantage of large image collection of Alibaba and state-of-the-art deep learning techniques to perform visual search at scale.  ...  Also, we propose a deep CNN model for joint detection and feature learning by mining user click behavior.  ...  Deep metric embedding: Deep metric learning is proved to yield impressive performance for measuring the similarity between images.  ... 
doi:10.1145/3219819.3219820 dblp:conf/kdd/ZhangPZZZRJ18 fatcat:caa42i3xmzfxhiddso5sjq6czy

Local Similarity-Aware Deep Feature Embedding [article]

Chen Huang, Chen Change Loy, Xiaoou Tang
2016 arXiv   pre-print
In this paper, we introduce a Position-Dependent Deep Metric (PDDM) unit, which is capable of learning a similarity metric adaptive to local feature structure.  ...  To make learning more effective and efficient, hard sample mining is usually employed, with samples identified through computing the Euclidean feature distance.  ...  To this end, we propose a new Position-Dependent Deep Metric (PDDM) unit for similarity metric learning. It is readily pluggable to train end-to-end with an existing deep embedding learning CNN.  ... 
arXiv:1610.08904v1 fatcat:bcw2yrx65neelddhqn5drbniki

Deep Learning for Person Re-identification: A Survey and Outlook [article]

Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, Steven C. H. Hoi
2021 arXiv   pre-print
We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and  ...  With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community.  ...  [154] present a point to set similarity for deep metric learning, which replace the point to point distance with the point to set metric.  ... 
arXiv:2001.04193v2 fatcat:4d3thmsr3va2tnu72nawlu2wxy

Neural Weighted A*: Learning Graph Costs and Heuristics with Differentiable Anytime A* [article]

Alberto Archetti, Marco Cannici, Matteo Matteucci
2021 arXiv   pre-print
We outperform similar architectures in planning accuracy and efficiency.  ...  Recently, the trend of incorporating differentiable algorithms into deep learning architectures arose in machine learning research, as the fusion of neural layers and algorithmic layers has been beneficial  ...  Their goal is to develop a deep-learning-based architecture able to learn improved cost functions such that the planning search avoids non-convenient regions to traverse.  ... 
arXiv:2105.01480v2 fatcat:d235srsrsvdmhj4dejuvoelxo4
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