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MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation [article]

Marvin Chancán, Michael Milford
2020 arXiv   pre-print
MVP temporally incorporates compact image representations, obtained using VPR, with optimized motion estimation data, including but not limited to those from VO or optimized radar odometry (RO), to efficiently  ...  Our MVP-based method can learn faster, and is more accurate and robust to both extreme environmental changes and poor GPS data than corresponding vision-only navigation methods.  ...  Our MVP method operates by temporally associating local estimates of motion with compact visual representations to efficiently train our policy network.  ... 
arXiv:2003.00667v1 fatcat:6d6xs2ri3rarbj6te3fry44eoa

Compact Global Descriptors for Visual Search

Vijay Chandrasekhar, Jie Lin, Olivier Morere, Antoine Veillard, Hanlin Goh
2015 2015 Data Compression Conference  
We study the problem of global descriptor compression in the context of image retrieval, focusing on extremely compact binary representations: 64-1024 bits.  ...  non-linear subspaces on which the data lie.  ...  Conclusion In this work, we study the problem of global descriptor compression in the context of image retrieval, focusing on extremely compact binary representations: 64-1024 bits.  ... 
doi:10.1109/dcc.2015.54 dblp:conf/dcc/ChandrasekharLM15 fatcat:mdrrky2pvfd33dvdhc7ih7oage

Visual Vocabulary Learning and Its Application to 3D and Mobile Visual Search [article]

Liujuan Cao
2012 arXiv   pre-print
Especial focuses would be also given for the recent trends in supervised/unsupervised vocabulary optimization, compact descriptor for visual search, as well as in multi-view based 3D object representation  ...  representation instead of the word-level representation.  ...  It is worth to note that, by exploiting semantics learning in visual representation stage, this kind of works differs from works that adopt semantic learning to refine the subsequent recognition stages  ... 
arXiv:1207.7244v1 fatcat:b6y7yvvcu5davkwh3zuv762qq4

CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples [article]

Filip Radenović, Giorgos Tolias, Ondřej Chum
2016 arXiv   pre-print
We show that both hard positive and hard negative examples enhance the final performance in particular object retrieval with compact codes.  ...  However, this achievement is preceded by extreme manual annotation in order to perform either training from scratch or fine-tuning for the target task.  ...  We extensively compare our results with the state-of-the-art performance on compact image representations and extremely short codes.  ... 
arXiv:1604.02426v3 fatcat:kz3swfz24fgarb3fj73khfgfbq

An unsupervised approach to Geographical Knowledge Discovery using street level and street network images [article]

Stephen Law, Mateo Neira
2019 arXiv   pre-print
The approach allows for meaningful explanations using a combination of geographical and generative visualisations to explore the latent space, and to show how the learned representation can be used to  ...  This research contributes to the ladder, where we show how latent variables learned from unsupervised learning methods on urbanimages can be used for geographic knowledge discovery.  ...  In the prediction experiment, we will study and compare the extent a PCA l in , a linear autoencoder and a non-linear autoencoder are able to learn a compact representation for different down-stream tasks  ... 
arXiv:1906.11907v2 fatcat:rs6xpkyxqjbade4g3mli5im35a

Compact Feature Representation for Image Classification Using ELMs

Dongshun Cui, Guanghao Zhang, Wei Han
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
In this paper, we have proposed a Compact Feature Representation algorithm (CFR-ELM) by using Extreme Learning Machine (ELM) under a shallow network framework.  ...  CFR-ELM consists of compact feature learning module and a post-processing module.  ...  Compact Feature Representation using ELM Overall Framework To learning compact features efficiently, we proposed a shallow feature-learning architecture using ELM and the framework is shown in Fig.2  ... 
doi:10.1109/iccvw.2017.124 dblp:conf/iccvw/CuiZH17 fatcat:fqpptwfz3rekzfty5i3sdxo25y

Aircraft Type Recognition in Remote Sensing Images: Bilinear Discriminative Extreme Learning Machine Framework

Baojun Zhao, Wei Tang, Yu Pan, Yuqi Han, Wenzheng Wang
2021 Electronics  
To solve the above problems, we propose the bilinear discriminative extreme learning machine (ELM) network (BD-ELMNet), which integrates the advantages of the CNN, autoencoder (AE), and ELM.  ...  Compared with the backpropagation (BP) optimization method, BD-ELMNet adopts a layer-by-layer training method without repeated adjustments to effectively learn discriminant features.  ...  This aspect helps to increase the compactness of the learning representation.  ... 
doi:10.3390/electronics10172046 fatcat:nl32ybgumra4bk6qs4uyirisja

Superpixel Estimation for Hyperspectral Imagery

Pegah Massoudifar, Anand Rangarajan, Paul Gader
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops  
Furthermore, superpixel estimation performed on the compact hyperspectral representation outperforms the same when executed on the entire volume.  ...  Due to the complexity and size of hyperspectral imagery and the enormous number of wavelength channels, the need for combining compact representations with image segmentation and superpixel estimation  ...  Future work will focus on dictionary learning to determine better sparse representations.  ... 
doi:10.1109/cvprw.2014.51 dblp:conf/cvpr/MassoudifarRG14 fatcat:hgfaezkyerfaxdnsmfufwqgjym

Page 382 of Mathematical Reviews Vol. 45, Issue 2 [page]

1973 Mathematical Reviews  
(errata insert) Let @ be a locally compact group and X a locally convex quasicomplete vector space.  ...  Here the basic prob- lems in the study of automorphic forms are reformulated in the theory of induced projective representations of a transitive locally compact group of automorphisms acting on a complex  ... 

COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [article]

Jiaxun Cui, Hang Qiu, Dian Chen, Peter Stone, Yuke Zhu
2022 arXiv   pre-print
Our model encodes LiDAR information into compact point-based representations that can be transmitted as messages between vehicles via realistic wireless channels.  ...  Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years.  ...  To reduce communication burdens, every V2V vehicle processes its own LiDAR data locally and encodes the raw 3D point clouds into keypoints, each associated with a compact representation learned by the  ... 
arXiv:2205.02222v1 fatcat:sg7rbexumngivn3acn2etpd4pm

Compact Sparse Coding for Ground-Based Cloud Classification

Shuang LIU, Zhong ZHANG, Xiaozhong CAO
2015 IEICE transactions on information and systems  
In this paper, we propose a novel coding strategy named compact sparse coding for groundbased cloud classification.  ...  Although sparse coding has emerged as an extremely powerful tool for texture and image classification, it neglects the relationship of coding coefficients from the same class in the training stage, which  ...  Feature Representation We learn a dictionary D k (k = 1, 2, . . . , C) for each class by utilizing Algorithm 1.  ... 
doi:10.1587/transinf.2015edl8095 fatcat:iva2gd7dvvbhbpkm6f3ma56noy

Compact Bilinear Pooling [article]

Yang Gao, Oscar Beijbom, Ning Zhang, Trevor Darrell
2016 arXiv   pre-print
Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets.  ...  We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions.  ...  Fig. 2 also shows performances using extremely low dimensional representation, d = 32, 128 and 512.  ... 
arXiv:1511.06062v2 fatcat:eithehndyjdcdfwk5o2urvx6qq

Compact Bilinear Pooling

Yang Gao, Oscar Beijbom, Ning Zhang, Trevor Darrell
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets.  ...  We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions.  ...  Fig. 2 also shows performances using extremely low dimensional representation, d = 32, 128 and 512.  ... 
doi:10.1109/cvpr.2016.41 dblp:conf/cvpr/GaoBZD16 fatcat:ik3q4hhngbf5nkwrsqt5u4bxza

Grouping Bilinear Pooling for Fine-Grained Image Classification

Rui Zeng, Jingsong He
2022 Applied Sciences  
This extreme compact representation largely overcomes the high redundancy of the full bilinear representation, the computational cost and storage consumption.  ...  In order to get compact bilinear representation, we propose grouping bilinear pooling (GBP) for fine-grained image classification in this paper.  ...  can be greatly preserved, and extreme compact bilinear feature representation is available.  ... 
doi:10.3390/app12105063 fatcat:iuflh7ccfrhunljma7pa4uc6lq

CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning [article]

Marvin Chancán, Michael Milford
2020 arXiv   pre-print
We first leverage place recognition and deep learning techniques combined with goal destination feedback to generate compact, bimodal image representations that can then be used to effectively learn control  ...  While deep reinforcement learning has shown success in solving these perception and decision-making problems in an end-to-end manner, these algorithms require large amounts of experience to learn navigation  ...  Policy Learning for Visual Navigation Our objective is to learn a policy for goal-directed navigation tasks using a compact, bimodal representation such as b t .  ... 
arXiv:1910.04335v2 fatcat:kuwwsso3qbbs7lxsiabus3ofum
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