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Learning a Deep Compact Image Representation for Visual Tracking
2013
Neural Information Processing Systems
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In contrast to most existing trackers which only learn the appearance of the tracked object online, we take a different approach, inspired by recent advances in deep learning architectures, by putting ...
Specifically, by using auxiliary natural images, we train a stacked denoising autoencoder offline to learn generic image features that are more robust against variations. ...
We believe that visual tracking can also benefit from deep learning for the same reasons. In this paper, we propose a novel deep learning tracker (DLT) for robust visual tracking. ...
dblp:conf/nips/WangY13
fatcat:buml7cvb7jhoref2merllntcsm
Learning Compact Target-Oriented Feature Representations for Visual Tracking
[article]
2019
arXiv
pre-print
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In particular, we learn compact, discriminative and target-oriented feature representations using the Laplacian coding algorithm that exploits the dependence among the input local features in a discriminative ...
To handle this problem, we propose a novel approach, which takes both advantages of good generalization of generative models and excellent discrimination of discriminative models, for visual tracking. ...
Different from them, we jointly learn the feature code and the correlation filter in a unified optimization framework so as to yield a more compact, discriminative and target-oriented feature representation ...
arXiv:1908.01442v1
fatcat:335wl2epdjggtc7iee2fqqtjpq
Visual Tracking with Online Incremental Deep Learning and Particle Filter
2015
International Journal of Signal Processing, Image Processing and Pattern Recognition
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To solve the problem of tracking the trajectory of a moving object and learning a deep compact image representation in the complex environment, a novel robust incremental deep learning tracker is presented ...
In this paper, we propose a novel incremental deep learning tracker (IDLT) for robust visual tracking based on particle filter framework. ...
Conclusions and Future Work This paper studies the visual tracking problem and presents a novel robust incremental deep learning tracker under the particle filter framework. ...
doi:10.14257/ijsip.2015.8.12.12
fatcat:jbc3qfzrrjfl5iaf52wl36jhvu
Visual robot localization using compact binary landmarks
2010
2010 IEEE International Conference on Robotics and Automation
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This paper is concerned with the problem of mobile robot localization using a novel compact representation of visual landmarks. ...
In this paper, we propose a compact binary code (e.g. 32bit code) landmark representation by employing the semantic hashing technique from web-scale image retrieval. ...
Visual vocabulary: semantic hashing The semantic hashing [11] aims to learn compact binary codes for image retrieval. ...
doi:10.1109/robot.2010.5509579
dblp:conf/icra/IkedaT10
fatcat:6ogi7h7t6bgebnq7eefgc3dule
Latent Representations of Terrain in Aerial Image Classification
2021
International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications
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The analysis of distributions demonstrated a landscape of compact concept clusters for most studied types of terrain with good separation between concept regions. ...
In this work we present a process of production and analysis of informative low-dimensional latent representations of real-world image data with neural network models of unsupervised generative learning ...
In the rest of the study, classes or categories of images were denoted with a symbol, such as "T" for transport tracks, "W" for wooded areas and so on. ...
dblp:conf/icteri/PrystavkaDCK21
fatcat:x6hupwx5bzh5hlzlfrtmy5wfvu
Learning Multi-view Deep Features for Small Object Retrieval in Surveillance Scenarios
2015
Proceedings of the 23rd ACM international conference on Multimedia - MM '15
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, which is as crucial as structure information for effective object representation. ...
In this paper, we propose to leverage discriminative Convolutional Neural Network (CNN) to learn deep structure and color feature to form an efficient multi-view object representation. ...
Using LSH, we encode each deep feature into one 256-bit binary code as a compact descriptor for visual object. The time cost is smaller for computing Hamming distance than Euclidean distance. ...
doi:10.1145/2733373.2806349
dblp:conf/mm/GuoWXZL15
fatcat:7gzu3u7yizbihdb4cofff423la
Table of Contents
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Özkalayci (missing), and Cevahir Çila (missing) Compact and Efficient Feature Representation and Learning in Computer Vision Robust Visual Tracking via Collaborative and Reinforced Convolutional Feature ...
Dolan (missing) Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Sur-Real: Frechet Mean and Distance Transform for Complex-Valued Deep Learning 889 MU-Net: Deep ...
Learning Raw Image Denoising With Bayer Pattern Unification and Bayer Preserving Augmentation 2070 Jiaming Liu (missing) , Chi-Hao Wu (missing) , Yuzhi Wang (missing) , Qin Xu (missing), Yuqian Zhou ...
doi:10.1109/cvprw.2019.00004
fatcat:h7xpqwyrofdxniqtxbodn66mpy
Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation
[chapter]
2019
Lecture Notes in Computer Science
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Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. ...
Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. ...
Conclusions We proposed Perception and Transfer for Reduced Architectures as a general framework to train compact models with knowledge transfer from traditional large deep learning models using gaze tracking ...
doi:10.1007/978-3-030-32251-9_43
pmid:31942569
pmcid:PMC6962054
fatcat:4gesdjordrh4demlforq3ggplm
Guest Editorial Introduction to the Special Section on Representation Learning for Visual Content Understanding
2020
IEEE transactions on circuits and systems for video technology (Print)
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Over the past years, his research interests have included multimedia analysis, machine learning, and image processing. In 2017, he received the IEEE SPS Best Paper Award. ...
He has served as a program committee member or reviewer for top conferences and prestigious journals. ...
Despite recent progresses on deep representation learning with a great amount of annotated data, how to effectively learn visual representation with limited data annotations still requires many efforts ...
doi:10.1109/tcsvt.2020.3009095
fatcat:5gew2gv32zg3tfwjaavrtknr2e
AI Oriented Large-Scale Video Management for Smart City: Technologies, Standards and Beyond
[article]
2017
arXiv
pre-print
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Deep learning has achieved substantial success in a series of tasks in computer vision. ...
Deep feature coding, instead of video coding, provides a practical solution for handling the large-scale video surveillance data. ...
Feature compression accounts for the conversion of raw deep features into compact representation bitstream. ...
arXiv:1712.01432v1
fatcat:7ollwfwufzbpfljdgqp2lodfcm
Compact Descriptors for Video Analysis: the Emerging MPEG Standard
[article]
2017
arXiv
pre-print
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This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG). ...
During the developments of MPEGCDVA, a series of techniques aiming to reduce the descriptor size and improve the video representation ability have been proposed. ...
deep learning features for image retrieval [18] , [19] . ...
arXiv:1704.08141v1
fatcat:7luecxbx75bwxlvabfnimymzlq
Feature Distilled Tracking
2017
IEEE Transactions on Cybernetics
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Finally, a scale adaptive discriminative correlation filter is learned on the distilled feature for visual tracking to handle scale variation of the target. ...
Feature extraction and representation is one of the most important components for fast, accurate, and robust visual tracking. ...
In the object tracking domain, Wang and Yeung [7] proposed a deep learning tracker by obtaining deep compact and informative image representations with a stacked denoising autoencoder network based on ...
doi:10.1109/tcyb.2017.2776977
pmid:29990247
fatcat:og6rciifafc7fcajru5qvc4j4y
Evolving deep unsupervised convolutional networks for vision-based reinforcement learning
2014
Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14
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Dealing with high-dimensional input spaces, like visual input, is a challenging task for reinforcement learning (RL). ...
The high-dimensional visual input, which the controller would normally receive, is first transformed into a compact feature vector through a deep, max-pooling convolutional neural network (MPCNN). ...
Acknowledgments This research was supported by Swiss National Science Foundation grant #138219: "Theory and Practice of Reinforcement Learning 2", and EU FP7 project: "NAnoSCale Engineering for Novel Computation ...
doi:10.1145/2576768.2598358
dblp:conf/gecco/KoutnikSG14
fatcat:5ew6mz3mlnfctnpewtcpirxjfe
Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle
2015
Pattern Recognition
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2016 pre-print arXiv:1604.04144v1 fatcat:unnd3bnkhvclxnjex5dznahtgy
Visual representation is crucial for a visual tracking method's performances. Conventionally, visual representations adopted in visual tracking rely on hand-crafted computer vision descriptors. ...
In this paper, we propose to learn complex-valued invariant representations from tracked sequential image patches, via strong temporal slowness constraint and stacked convolutional autoencoders. ...
DLT is especially relevant because of its use of deep denoising autoencoders to learn a compact representation online for tracking. ...
doi:10.1016/j.patcog.2015.02.012
fatcat:26lq2q5uvnduxcdtiqttm3cgui
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TMM 2021 120-132 STGL: Spatial-Temporal Graph Representation and Learning for Visual Tracking. ...
., +, TMM 2021 3215-3226 Deep learning A Bottom-Up and Top-Down Integration Framework for Online Object Tracking. ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
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