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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
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.  ...  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  ...  An adaptive weighted triplet loss is introduced in [43] to balance the positive and negative triplet using the similarity difference.  ... 
arXiv:2001.04193v2 fatcat:4d3thmsr3va2tnu72nawlu2wxy

Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking [article]

Jae Shin Yoon, Takaaki Shiratori, Shoou-I Yu, Hyun Soo Park
2019 arXiv   pre-print
Then, we overcome the domain mismatch between lab and uncontrolled environments by performing self-supervised domain adaptation based on "consecutive frame texture consistency" based on the assumption  ...  In this paper, we propose a self-supervised domain adaptation approach to enable the animation of high-fidelity face models from a commodity camera.  ...  Our method is also able to adapt to the white-balance of the current scene. Note that the gaze direction is also tracked for most cases.  ... 
arXiv:1907.10815v1 fatcat:5kvwovdderetto73mcjki4x67i

Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis [article]

Changhong Fu, Kunhan Lu, Guangze Zheng, Junjie Ye, Ziang Cao, Bowen Li, Geng Lu
2022 arXiv   pre-print
As an emerging force in the revolutionary trend of deep learning, Siamese networks shine in UAV-based object tracking with their promising balance of accuracy, robustness, and speed.  ...  Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of intelligent transportation systems because of its  ...  ACKNOWLEDGEMENT This work is supported by the National Natural Science Foundation of China (No. 62173249) and the Natural Science Foundation of Shanghai (No. 20ZR1460100).  ... 
arXiv:2205.04281v2 fatcat:kaujdfb7ivdtxeiz44lu36oqum

Robust Tracking Using Region Proposal Networks [article]

Jimmy Ren, Zhiyang Yu, Jianbo Liu, Rui Zhang, Wenxiu Sun, Jiahao Pang, Xiaohao Chen, Qiong Yan
2017 arXiv   pre-print
In this paper we discovered that the internal structure of Region Proposal Network (RPN)'s top layer feature can be utilized for robust visual tracking.  ...  However, due to the drastic difference between image classification and tracking, extra treatments such as model ensemble and feature engineering must be carried out to bridge the two domains.  ...  The issue with this method however is that it is very hard to hand-pick the best group of lower level feature maps which consistently provides relevant features across scenes and domains.  ... 
arXiv:1705.10447v1 fatcat:e6odonbkojblxhbndi3weip224

TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers [article]

Xuyang Bai, Zeyu Hu, Xinge Zhu, Qingqiu Huang, Yilun Chen, Hongbo Fu, Chiew-Lan Tai
2022 arXiv   pre-print
We additionally design an image-guided query initialization strategy to deal with objects that are difficult to detect in point clouds.  ...  The attention mechanism of the transformer enables our model to adaptively determine where and what information should be taken from the image, leading to a robust and effective fusion strategy.  ...  This work is supported by Hong Kong RGC (GRF 16206819, 16203518, T22-603/15N), Guangzhou Okay Information Technology with the project GZETDZ18EG05, and City University of Hong Kong (No. 7005729).  ... 
arXiv:2203.11496v1 fatcat:surivv4iyjbsjm4ufcnsymdpka

Siamese Visual Object Tracking: A Survey

Milan Ondrasovic, Peter Tarabek
2021 IEEE Access  
Additionally, they extended the classical Siamese framework by a generalized focal logistic loss [81] to mine hard negative samples.  ...  The tracking process is thus interfered with by similarity. Siamese networks have drawn great attention due to their balanced accuracy and speed.  ...  Trackers marked with symbol "*" deal with a specific task of TIR tracking and not standard VOT. Tracker Year  ... 
doi:10.1109/access.2021.3101988 fatcat:iwjlqirwqrav5nadbaw2g5huuu

2021 Index IEEE Robotics and Automation Letters Vol. 6

2021 IEEE Robotics and Automation Letters  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, LRA April 2021 1981-1988 AUV Tool Manipulation With Hard and Soft Actuators.  ... 
doi:10.1109/lra.2021.3119726 fatcat:lsnerdofvveqhlv7xx7gati2xu

A Cyclic Information–Interaction Model for Remote Sensing Image Segmentation

Xu Cheng, Lihua Liu, Chen Song
2021 Remote Sensing  
Additionally, we further propose an adaptively cyclic feature information–interaction model, which adopts branch prediction to decide the number of visual perceptions, accomplishing adaptive fusion of  ...  feature maps of different sources, completing extraction, fusion, and enhancement of global semantic features with local contextual information of the object.  ...  Acknowledgments: The authors acknowledge the academic editors and the anonymous reviewers for their insightful comments and suggestions, helping to improve quality and acceptability of the manuscript.  ... 
doi:10.3390/rs13193871 fatcat:euefdvpidrckdpntdgfi3saari

Deep Learning for Face Anti-Spoofing: A Survey [article]

Zitong Yu, Yunxiao Qin, Xiaobai Li, Chenxu Zhao, Zhen Lei, Guoying Zhao
2022 arXiv   pre-print
., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial  ...  It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g  ...  [138] adopt the binary focal loss to guide the model to enlarge the margin between live/spoof samples and achieve strong discrimination for hard samples.  ... 
arXiv:2106.14948v2 fatcat:wsheo7hbwvewhjoe6ykwjuqfii

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
; Xu, Yanyan; Ke, Dengfeng 1153 Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection DAY 3 -Jan 14, 2021 Cheng, Ming; Cai, Kunjing; Li, Ming 1171 RWF  ...  Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training DAY 4 -Jan 15, 2021 Liu, Xinyue; Yang, Shichong; Zong, Linlin 883 Constrained Spectral Clustering Network with Self-Training  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences – Version 1 [article]

Imme Ebert-Uphoff, Ryan Lagerquist, Kyle Hilburn, Yoonjin Lee, Katherine Haynes, Jason Stock, Christina Kumler, Jebb Q. Stewart
2021 arXiv   pre-print
, discrete and soft discretization, and concepts such as focal, robust, and adaptive loss.  ...  While examples are currently provided in this guide for Python with Keras and the TensorFlow backend, the basic concepts also apply to other environments, such as Python with PyTorch.  ...  Ebert-Uphoff acknowledges support for this work by the National Science Foundation under NSF grant ICER-2019758 (NSF AI institute) and NSF grant OAR-1934668.  ... 
arXiv:2106.09757v1 fatcat:t2lnh637ivbv3m4m5q4vob4fva

A Survey on Deep Learning-based Single Image Crowd Counting: Network Design, Loss Function and Supervisory Signal [article]

Haoyue Bai, Jiageng Mao, S.-H. Gary Chan
2022 arXiv   pre-print
, loss functions, and supervisory signals.  ...  We study and compare the approaches using the public datasets and evaluation metrics. We conclude the survey with some future directions.  ...  DCANet [209] introduces a domain-guided channel attention network to guide the extraction of domain-specific feature representation for multi-domain crowd counting.  ... 
arXiv:2012.15685v2 fatcat:kvrqnczkgbdxdnvr4243atvj3e

Video Synopsis Based on Attention Mechanism and Local Transparent Processing

Shengbo Chen, Xianrui Liu, Yiyong Huang, Congcong Zhou, Huaikou Miao
2020 IEEE Access  
This paper proposes an approach to generating video synopsis with complete foreground and clearer trajectory of moving objects.  ...  Then, combining integrating the attention-RetinaNet with Local Transparency-Handling Collision (LTHC) algorithm is given out which results in the trajectory combination optimization and makes the trajectory  ...  ACKNOWLEDGMENT The authors thank the anonymous referee for a careful checking of the details and for helpful comments that improved this paper.  ... 
doi:10.1109/access.2020.2994613 fatcat:b32oxlw5tvbv5p65fm2am5aija

Imbalance Problems in Object Detection: A Review [article]

Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas
2020 arXiv   pre-print
Researchers can track newer studies on this webpage available at: .  ...  Following this taxonomy, we discuss each problem in depth and present a unifying yet critical perspective on the solutions in the literature.  ...  ACKNOWLEDGMENTS This work was partially supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) through the project titled "Object Detection in Videos with Deep Neural Networks  ... 
arXiv:1909.00169v3 fatcat:4gzhgl2mirg6zcc2g63e5ha6zi

A Survey of Deep Learning-based Object Detection

Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhixi Feng, Rong Qu
2019 IEEE Access  
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with  ...  With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved.  ...  [33] propose a loss function, called focal loss, which can down-weight the loss assigned to well-classified or easy examples, focusing on the hard training examples and avoiding the vast number of easy  ... 
doi:10.1109/access.2019.2939201 fatcat:jesz2av2tjbkxfpaqyecptgls4
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