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Using Deep Learning for Visual Decoding and Reconstruction from Brain Activity: A Review [article]

Madison Van Horn
2021 arXiv   pre-print
This literature review will discuss the use of deep learning methods for image reconstruction using fMRI data.  ...  This paper will conclude the use of deep learning within visual decoding and reconstruction is highly optimal when using variations of deep neural networks and will provide details of potential future  ...  However, this initial implementation further increased interest in using deep learning for visual reconstruction.  ... 
arXiv:2108.04169v1 fatcat:jnrfc6nzgrdwhplpdnzz6ntk7y

Deep Learning for Visual SLAM in Transportation Robotics: A review

Chao Duan, Steffen Junginger, Jiahao Huang, Kairong Jin, Kerstin Thurow
2019 Transportation Safety and Environment  
In this paper, the latest research progress of deep learning applied to the field of visual SLAM is reviewed.  ...  With the great achievements of deep learning methods in the field of computer vision, there is a trend of applying deep learning methods to visual SLAM.  ...  This paper provides a review of deep learning methods applied on visual SLAM, including the advantagse and limitations of the different deep learning methods.  ... 
doi:10.1093/tse/tdz019 fatcat:c5tj64xro5ftvcw6qwz7rgrgky

Visual Explanation for Deep Metric Learning [article]

Sijie Zhu, Taojiannan Yang, Chen Chen
2021 arXiv   pre-print
This work explores the visual explanation for deep metric learning and its applications.  ...  We show that the proposed framework can be directly deployed to a large range of metric learning applications and provides valuable information for understanding the model.  ...  Section II provides a brief review on existing visual interpretation methods for classification and metric learning.  ... 
arXiv:1909.12977v4 fatcat:cjpodf7d2zh2bbfn2tjrkvq3ka

Visual Analytics for Explainable Deep Learning [article]

Jaegul Choo, Shixia Liu
2018 arXiv   pre-print
In this paper, we review visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discuss potential challenges and future research directions.  ...  Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever.  ...  visual analytics for deep learning, user-driven generative models, and visual analytics for secure deep learning.  ... 
arXiv:1804.02527v1 fatcat:efwpg3ot5nfgfnbhnlt6rfkm44

Deep Learning Techniques for Visual Counting [article]

Luca Ciampi
2022 arXiv   pre-print
In this dissertation, we investigated and enhanced Deep Learning (DL) techniques for counting objects, like pedestrians, cells or vehicles, in still images or video frames.  ...  that is responsible for a significant drop in performance at inference time when new scenarios are presented to these algorithms.  ...  For example, Chopra et al. [47] proposed a model called deep learning for DA by interpolating between domains (DLID).  ... 
arXiv:2206.03033v2 fatcat:johyxd4slrcjlad36qwwbk7l3q

Visual Interpretability for Deep Learning: a Survey [article]

Quanshi Zhang, Song-Chun Zhu
2018 arXiv   pre-print
This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations.  ...  We focus on convolutional neural networks (CNNs), and we revisit the visualization of CNN representations, methods of diagnosing representations of pre-trained CNNs, approaches for disentangling pre-trained  ...  neural patterns in conv-layers of a pre-trained CNN and build a model for hierarchical object understanding.  ... 
arXiv:1802.00614v2 fatcat:g55ax3lso5axtb6cn7munbaidi

Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

Liping Yang, Alan MacEachren, Prasenjit Mitra, Teresa Onorati
2018 ISPRS International Journal of Geo-Information  
Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep  ...  This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning.  ...  Acknowledgments: The authors are grateful to NVIDIA for awarding one Titan X Pascal GPU to support this  ... 
doi:10.3390/ijgi7020065 fatcat:2vjluil5zfbvzmlvbvbb7i433q

Deep Cross Residual Learning for Multitask Visual Recognition [article]

Brendan Jou, Shih-Fu Chang
2016 arXiv   pre-print
We propose a novel extension of residual learning for deep networks that enables intuitive learning across multiple related tasks using cross-connections called cross-residuals.  ...  Residual learning has recently surfaced as an effective means of constructing very deep neural networks for object recognition.  ...  Acknowledgements We thank our reviewers for their helpful and constructive feedback.  ... 
arXiv:1604.01335v2 fatcat:gtlyqtyjajhd5pfdkosu4gumoy

Master's Thesis : Deep Learning for Visual Recognition [article]

Rémi Cadène, Nicolas Thome, Matthieu Cord
2016 arXiv   pre-print
Our last contribution is a framework, build on top of Torch7, for training and testing deep models on any visual recognition tasks and on datasets of any scale.  ...  We first draw up a state-of-the-art review of the Convolutional Neural Networks aiming to understand the history behind this family of statistical models, the limit of modern architectures and the novel  ...  Nicolas Thome for supervising my research on this project and providing resources for the experiments. Additionally, I thank all the people at LIP6 for the perfect working atmosphere.  ... 
arXiv:1610.05567v1 fatcat:hdgwemcxrrhgxmesddanftmtba

Evaluating the Progress of Deep Learning for Visual Relational Concepts [article]

Sebastian Stabinger, Peer David, Justus Piater, Antonio Rodríguez-Sánchez
2021 arXiv   pre-print
We will review deep learning research that is linked to relational concept learning, even if it was not originally presented from this angle.  ...  Convolutional Neural Networks (CNNs) have become the state of the art method for image classification in the last ten years.  ...  Current Research on Deep Learning for Visual Relational Concepts Since most of the research on deep learning is concerned with perceptual concept learning and the systems perform very well on these tasks  ... 
arXiv:2001.10857v3 fatcat:k5c5fhnxfza2dmwrwb35bnocsm

Deep Learning for Underwater Visual Odometry Estimation

Bernardo Teixeira, Hugo Silva, Anibal Matos, Eduardo Silva
2020 IEEE Access  
application domains, has prompted a great a volume of recent research concerning Deep Learning architectures tailored for visual odometry estimation.  ...  INDEX TERMS Artificial intelligence, computer vision, deep learning, visual odometry, robot navigation, visual SLAM.  ...  Building upon the success of deep learning frameworks for some visual tasks, a lot of research has been devoted to taking advantage of deep learning potential for a wider range of tasks.  ... 
doi:10.1109/access.2020.2978406 fatcat:zjjpiqgol5bclksbob6lnrf2lu

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers [article]

Fred Hohman, Minsuk Kahng, Robert Pienta, Duen Horng Chau
2018 arXiv   pre-print
As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how  ...  We present a survey of the role of visual analytics in deep learning research, which highlights its short yet impactful history and thoroughly summarizes the state-of-the-art using a human-centered interrogative  ...  Human-AI Pairing Much of this survey is dedicated towards reviewing the state-of-the-art in visual analytics for deep learning, with a focus on interpretability.  ... 
arXiv:1801.06889v3 fatcat:c5x3ftcf5fbapc5tsyhm5w2dhq

Deep learning hashing for mobile visual search

Wu Liu, Huadong Ma, Heng Qi, Dong Zhao, Zhineng Chen
2017 EURASIP Journal on Image and Video Processing  
Finally, we demonstrate a case study of deep learning hashing based mobile visual search system.  ...  In this paper, we explore to holistically exploit the deep learning-based hashing methods for more robust and instant mobile visual search.  ...  Section 1.2 reviews mobile visual search work. Section 1.3 surveys the deep learning hashing methods. Section 1.4 introduces the newest deep learning optimization schemes.  ... 
doi:10.1186/s13640-017-0167-4 fatcat:vcdhjjbe6jai7hyigxstihcega

Deep Learning for Visual Tracking: A Comprehensive Survey [article]

Seyed Mojtaba Marvasti-Zadeh, Li Cheng, Hossein Ghanei-Yakhdan, and Shohreh Kasaei
2019 arXiv   pre-print
been developed and demonstrated with significant progress in recent years -- predominantly by recent deep learning (DL)-based methods.  ...  It may serve as a gentle use guide for practitioners to weigh on when and under what conditions to choose which method(s).  ...  Kamal Nasrollahi (Visual Analysis of People Lab (VAP), Aalborg University) for his beneficial comments.  ... 
arXiv:1912.00535v1 fatcat:v5ikqi2cpbblhgtkiu6z6l5anq

Deep Reinforcement Learning for Visual Object Tracking in Videos [article]

Da Zhang, Hamid Maei, Xin Wang, Yuan-Fang Wang
2017 arXiv   pre-print
In this paper we introduce a fully end-to-end approach for visual tracking in videos that learns to predict the bounding box locations of a target object at every frame.  ...  Based on this intuition, we formulate our model as a recurrent convolutional neural network agent that interacts with a video overtime, and our model can be trained with reinforcement learning (RL) algorithms  ...  Other than CNN-based trackers, our paper aims to develop a new paradigm for solving the visual tracking problem by bringing in RNN and RL to explicitly exploit temporal correlation in videos.  ... 
arXiv:1701.08936v2 fatcat:csvjdoftvffrrnrsvtvpkpcq6u
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