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Large-Scale Image Retrieval with Attentive Deep Local Features [article]

Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, Bohyung Han
2018 arXiv   pre-print
We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature).  ...  To identify semantically useful local features for image retrieval, we also propose an attention mechanism for keypoint selection, which shares most network layers with the descriptor.  ...  Figure 1 : 1 Overall architecture of our image retrieval system, using DEep Local Features (DELF) and attention-based keypoint selection.  ... 
arXiv:1612.06321v4 fatcat:tadtcno7rbhojnz27qxugojvmi

SuperGlue: Learning Feature Matching With Graph Neural Networks

Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
We introduce a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly.  ...  This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.  ...  SuperGlue can retrieve or attend based on both appearance and keypoint location as they are encoded in the representation x i .  ... 
doi:10.1109/cvpr42600.2020.00499 dblp:conf/cvpr/SarlinDMR20 fatcat:ltrmgcy3pvdbxgu3nbp4iwdsai

DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving [article]

Yao Zhou, Guowei Wan, Shenhua Hou, Li Yu, Gang Wang, Xiaofei Rui, Shiyu Song
2020 arXiv   pre-print
We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy.  ...  In this work, we seek to exploit the deep attention mechanism to search for salient, distinctive and stable features that are good for long-term matching in the scene through a novel end-to-end deep neural  ...  Based on the attention scores from the heatmap, the AKS module selects good features from the map image as the keypoints.  ... 
arXiv:2003.03026v2 fatcat:kh3n5gfopbgcfkm54cckfxnlxi

A Novel Feature Aggregation Approach for Image Retrieval Using Local and Global Features

Yuhua Li, Zhiqiang He, Junxia Ma, Zhifeng Zhang, Wangwei Zhang, Prasenjit Chatterjee, Dragan Pamucar
2022 CMES - Computer Modeling in Engineering & Sciences  
The method first extracts global and local features of the input image and then selects keypoints from local features by using the attention mechanism.  ...  The current deep convolution features based on retrieval methods cannot fully use the characteristics of the salient image regions.  ...  And we trained the network with standard cross-entropy loss for images classification. The second step is the keypoint selection for local feature descriptors based on attention mechanism.  ... 
doi:10.32604/cmes.2022.016287 fatcat:dtsswsthnvak7noirxjhxmwvp4

DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization [article]

Juan Du, Rui Wang, Daniel Cremers
2020 arXiv   pre-print
We generate the global descriptor by directly aggregating the learned local descriptors with an effective attention mechanism.  ...  To this end, we design a Siamese network that jointly learns 3D local feature detection and description directly from raw 3D points.  ...  ., the keypoint detector and the attention map predictor. 3D Keypoint detector. Fig. 6 depicts the architecture of our 3D keypoint detector.  ... 
arXiv:2007.09217v1 fatcat:yzzwgcz7tnedtnatqvshgoauxq

Seeing the Big Picture: Deep Embedding with Contextual Evidences [article]

Liang Zheng, Shengjin Wang, Fei He, Qi Tian
2014 arXiv   pre-print
CNN has been shown to produce excellent performance on a dozen computer vision tasks such as image classification and detection, but few works have been done on BoW based image retrieval.  ...  In the Bag-of-Words (BoW) model based image retrieval task, the precision of visual matching plays a critical role in improving retrieval performance.  ...  Second, the successful CNN activation feature is rarely used in BoW based image retrieval.  ... 
arXiv:1406.0132v1 fatcat:lybelhi26nh6xck4gjn7monemm

Pose-guided Inter- and Intra-part Relational Transformer for Occluded Person Re-Identification [article]

Zhongxing Ma, Yifan Zhao, Jia Li
2021 arXiv   pre-print
The use of local information for feature extraction and matching is still necessary.  ...  The intra-part module creates local relations with mask-guided features, while the inter-part relationship builds correlations with transformers, to develop cross relationships between part nodes.  ...  ACKNOWLEDGEMENT This work was supported by grants from National Natural Science Foundation of China (No. 61922006) and CAAI-Huawei MindSpore Open Fund.  ... 
arXiv:2109.03483v1 fatcat:4cpwmkkqkrccnbiy3dqiaoqbjm

Video Logo Retrieval based on local Features [article]

Bochen Guan, Hanrong Ye, Hong Liu, William A. Sethares
2020 arXiv   pre-print
VLR uses local features to overcome the weakness of global feature-based models such as convolutional neural networks (CNN).  ...  This paper develops an algorithm called Video Logo Retrieval (VLR), which is an image-to-video retrieval algorithm based on the spatial distribution of local image descriptors that measure the distance  ...  local feature matching keypoints.  ... 
arXiv:1808.03735v4 fatcat:omn2ofahcngsdck6ns2dghewya

SuperGlue: Learning Feature Matching with Graph Neural Networks [article]

Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich
2020 arXiv   pre-print
We introduce a flexible context aggregation mechanism based on attention, enabling SuperGlue to reason about the underlying 3D scene and feature assignments jointly.  ...  This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.  ...  SuperGlue can retrieve or attend based on both appearance and keypoint location as they are encoded in the representation x i .  ... 
arXiv:1911.11763v2 fatcat:sb7qcoyji5dl7hmdsrs6ymvwla

Local Deep Descriptors in Bag-of-Words for Image Retrieval

Jiewei Cao, Zi Huang, Heng Tao Shen
2017 Proceedings of the on Thematic Workshops of ACM Multimedia 2017 - Thematic Workshops '17  
Speci cally, we show how to use the CNN as a combination of local feature detector and extractor, without the need of feeding multiple image patches to the network.  ...  The Bag-of-Words (BoW) models using the SIFT descriptors have achieved great success in content-based image retrieval over the past decade.  ...  INTRODUCTION Content-based image retrieval (CBIR) has been an active research topic since the seminal work of Sivic and Zisserman [38] , which is based on the Bag-of-Words (BoW) model using local descriptor  ... 
doi:10.1145/3126686.3127018 dblp:conf/mm/CaoHS17 fatcat:pudntrtbjnbzpmcsfj2ytplqga

Unifying Deep Local and Global Features for Image Search [article]

Bingyi Cao, Andre Araujo, Jack Sim
2020 arXiv   pre-print
In this work, our key contribution is to unify global and local features into a single deep model, enabling accurate retrieval with efficient feature extraction.  ...  We refer to the new model as DELG, standing for DEep Local and Global features.  ...  The model is based on a ResNet backbone, leveraging generalized mean pooling to produce global features and attention-based keypoint detection to produce local features.  ... 
arXiv:2001.05027v4 fatcat:innrm2vs65bxdf3v3ri3zjftiq

From handcrafted to deep local features [article]

Gabriela Csurka, Christopher R. Dance, Martin Humenberger
2019 arXiv   pre-print
This paper presents an overview of the evolution of local features from handcrafted to deep-learning-based methods, followed by a discussion of several benchmarks and papers evaluating such local features  ...  We first present handcrafted methods, followed by methods based on classical machine learning and finally we discuss methods based on deep-learning.  ...  Evaluation was done on three benchmark tasks: patch verification, image matching and patch retrieval.  ... 
arXiv:1807.10254v3 fatcat:youj6ggjqja7rmv2rocinm252y

Learning to Match Features with Seeded Graph Matching Network [article]

Hongkai Chen, Zixin Luo, Jiahui Zhang, Lei Zhou, Xuyang Bai, Zeyu Hu, Chiew-Lan Tai, Long Quan
2021 arXiv   pre-print
seed features and exchanges messages across images. 3) Attentional Unpooling, which propagates seed features back to original keypoints.  ...  Matching local features across images is a fundamental problem in computer vision.  ...  learning-based local features [26, 11].  ... 
arXiv:2108.08771v1 fatcat:b6fgvvs2yrccdjdvaseq4wiidq

Deep Learning-based Occluded Person Re-identification: A Survey [article]

Yunjie Peng, Saihui Hou, Chunshui Cao, Xu Liu, Yongzhen Huang, Zhiqiang He
2022 arXiv   pre-print
With the promotion of deep learning technology and the increasing demand for intelligent video surveillance, the frequent occlusion in real-world applications has made occluded person Re-ID draw considerable  ...  Occluded person re-identification (Re-ID) aims at addressing the occlusion problem when retrieving the person of interest across multiple cameras.  ...  PFD [52] generates local patch features and multiplies them with the processed keypoint heatmaps element-wisely to obtain the pose-guided features.  ... 
arXiv:2207.14452v1 fatcat:egnltk4epndh5ovxaz3zocicim

ContextDesc: Local Descriptor Augmentation with Cross-Modality Context [article]

Zixin Luo, Tianwei Shen, Lei Zhou, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, Long Quan
2019 arXiv   pre-print
Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations.  ...  The proposed augmentation scheme is lightweight compared with the raw local feature description, meanwhile improves remarkably on several large-scale benchmarks with diversified scenes, which demonstrates  ...  In arXiv, 2016. 6, 11 scale image retrieval with attentive deep local features. In [7] J. M. Dmytro Mishkin, Filip Radenovic.  ... 
arXiv:1904.04084v1 fatcat:gd2pe42w75bbjazhw33z4pwiwe
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