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cloud computing, software architecture, video signal processing,
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Balanced scorecard, Business intelligence, Data warehouse,
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Survey, Product detection, Product recognition,
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Detection, Grocery products, Non maximal suppression,
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Cameras, Pose estimation, Robot vision systems,
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Fine-grained classification, Reconstruction-classification network,
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Deep learning, Visualization, Perturbation methods,
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Shen, J.[Jialie],
Zhang, J.[Jian],
Xu, J.S.[Jing-Song],
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Multimodal Marketing Intent Analysis for Effective Targeted
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2204
Media, Advertising, Feature extraction, Social networking (online),
Task analysis, Springs, Visualization, Multimodal, targeted advertising
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Chen, H.[Hao],
Zhou, Y.Z.[Yang-Zhun],
Li, J.[Jun],
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IP(31), 2022, pp. 3004-3016.
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2205
Task analysis, Training, Annotations, Testing, Object detection,
Feature extraction, Deep learning, Automatic check-out,
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2205
Gap detection, Retail store, Graph convolutional network,
Siamese network, Structural support vector machine
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Guo, Z.Y.[Zhao-Yu],
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TaoHighlight: Commodity-Aware Multi-Modal Video Highlight Detection
in E-Commerce,
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2205
Task analysis, Visualization, Feature extraction, Data models,
Streaming media, Linguistics, Convolution, multi-modal learning
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Siddiqui, T.[Tarique],
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Expressive Querying for Accelerating Visual Analytics,
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Zhang, G.[Gengwei],
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Caption-Aided Product Detection via Collaborative Pseudo-Label
Harmonization,
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IEEE DOI
2306
Training, Detectors, Electronic commerce, Noise measurement, Head,
Proposals, Object detection, Product detection, pseudo-label, positive mining
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Li, H.Y.[Hao-Yuan],
Jiang, H.[Hao],
Jin, T.[Tao],
Li, M.Y.[Meng-Yan],
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Lin, Z.J.[Zhi-Jie],
Zhao, Y.[Yang],
Zhao, Z.[Zhou],
DATE: Domain Adaptive Product Seeker for E-Commerce,
CVPR23(19315-19324)
IEEE DOI
2309
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Chen, H.[Hao],
Wei, X.S.[Xiu-Shen],
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MultMed(25), 2023, pp. 9147-9160.
IEEE DOI
2312
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Chen, H.[Hao],
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Xu, H.[Hui],
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Automatic Check-Out via Prototype-Based Classifier Learning from
Single-Product Exemplars,
ECCV22(XXV:277-293).
Springer DOI
2211
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Liu, Y.[Yuan],
Product Image Recommendation with Transformer Model Using Deep
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IJIG(23), No. 6 2023, pp. 2550020.
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Tiribelli, S.[Simona],
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technology for consumer's behaviour understanding,
CVIU(248), 2024, pp. 104142.
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2409
Human behaviour analysis, Artificial intelligence, AI ethics, Retail environment
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Yuan, Z.Z.[Zhong-Zheng],
Rawlekar, S.[Samyak],
Garg, S.[Siddharth],
Erkip, E.[Elza],
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Split Computing With Scalable Feature Compression for Visual
Analytics on the Edge,
MultMed(26), 2024, pp. 10121-10133.
IEEE DOI
2410
Image coding, Computational modeling, Task analysis, Servers,
Performance evaluation, Bit rate, Analytical models, split computing
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Jia, M.[Meihuizi],
Shen, L.[Lei],
Tuan, L.A.[Luu Anh],
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Xu, J.[Jing],
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Yuan, S.[Shaozu],
He, X.D.[Xiao-Dong],
MuJo-SF: Multimodal Joint Slot Filling for Attribute Value Prediction
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IEEE DOI
2410
Electronic commerce, Task analysis, Data mining, Feature extraction,
Visualization, Training, Filling, e-commerce commodity
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Ciapas, B.[Bernardas],
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computer vision, image classification, image matching, image recognition
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Sheshappanavar, S.V.[Shivanand Venkanna],
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Wang, Y.F.[Yu-Fan],
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3DV24(516-527)
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WWW Link.
2408
Point cloud compression, Training, Solid modeling,
Benchmark testing, Robot sensing systems, Mobile handsets, Grocery,
3D Class-Incremental Learning
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Self-supervised Human-Object Interaction of Complex Scenes with
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SmallData24(744-751)
IEEE DOI
2404
Analytical models, Consumer behavior, Computational modeling,
Self-supervised learning, Transformers
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Bhattacharya, G.[Gaurab],
Sharma, G.[Gaurav],
Chatterjee, K.[Kallol],
Chakrapani,
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Pal, A.[Arpan],
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PMTL: A Progressive Multi-Level Training Framework for Retail
Taxonomy Classification,
SmallData24(736-743)
IEEE DOI
2404
Training, Taxonomy, Decision making, Manuals, Feature extraction
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Chen, F.[Fulu],
Wei, X.W.[Xiao-Wei],
Yu, S.S.[Sai-Sai],
Ma, P.F.[Peng-Fei],
He, S.Q.[Shu-Qing],
Customer Churn Prediction based on Stacking Model,
CVIDL23(518-521)
IEEE DOI
2403
Logistic regression, Computational modeling, Stacking, Companies,
Predictive models, Credit cards, Stacking model, logistic regression
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Tolja, K.[Katarina],
Subašic, M.[Marko],
Kalafatic, Z.[Zoran],
Loncaric, S.[Sven],
Enhancing Retail Product Recognition: Fine-Grained Bottle Size
Classification,
MVA23(1-5)
DOI Link
2403
Machine vision, Data models
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Bai, X.H.[Xue-Han],
Li, Y.[Yan],
Cheng, Y.H.[Yan-Hua],
Yang, W.J.[Wen-Jie],
Chen, Q.[Quan],
Li, H.[Han],
Cross-Domain Product Representation Learning for Rich-Content
E-Commerce,
ICCV23(5674-5683)
IEEE DOI
2401
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Yang, W.J.[Wen-Jie],
Chen, Y.[Yiyi],
Li, Y.[Yan],
Cheng, Y.H.[Yan-Hua],
Liu, X.D.[Xu-Dong],
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ICCV23(13358-13367)
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2401
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Autonomous mobile robot for automatic out of stock detection in a
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ACVR23(1821-1830)
IEEE DOI
2401
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Li, Z.X.[Zhi-Xuan],
Ye, W.N.[Wei-Ning],
Terven, J.[Juan],
Bennett, Z.[Zachary],
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Huang, T.J.[Tie-Jun],
MUVA: A New Large-Scale Benchmark for Multi-view Amodal Instance
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ICCV23(23447-23456)
IEEE DOI
2401
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Strohmayer, J.[Julian],
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Domain-adaptive Data Synthesis for Large-scale Supermarket Product
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Springer DOI
2312
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Morán, E.F.[Emmanuel F.],
Vintimilla, B.X.[Boris X.],
Realpe, M.A.[Miguel A.],
Towards a Robust Solution for the Supermarket Shelf Audit Problem:
Obsolete Price Tags in Shelves,
CIARP23(I:257-271).
Springer DOI
2312
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Strohmayer, J.[Julian],
Kampel, M.[Martin],
Real-Time Supermarket Product Recognition on Mobile Devices Using
Scalable Pipelines,
ICIP23(420-424)
IEEE DOI
2312
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Ma, Z.L.[Ze-Liang],
Liu, D.[Delong],
Cui, Z.[Zhe],
Zhao, Y.[Yanyun],
AdaptCD: An Adaptive Target Region-based Commodity Detection System,
AICity23(5486-5495)
IEEE DOI
2309
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Cai, Y.C.[Yi-Chen],
Jiao, A.[Aoran],
DACNet: A Deep Automated Checkout Network with Selective Deblurring,
AICity23(5278-5286)
IEEE DOI
2309
BibRef
Jin, Y.[Yang],
Li, Y.Z.[Yong-Zhi],
Yuan, Z.H.[Ze-Huan],
Mu, Y.D.[Ya-Dong],
Learning Instance-Level Representation for Large-Scale Multi-Modal
Pretraining in E-Commerce,
CVPR23(11060-11069)
IEEE DOI
2309
BibRef
Shi, Z.Q.[Zi-Qiang],
Liu, Z.L.[Zhong-Ling],
Liu, L.[Liu],
Liu, R.J.[Ru-Jie],
Yamamoto, T.[Takuma],
Mi, X.Y.[Xiao-Yu],
Uchida, D.[Daisuke],
CheckSORT: Refined Synthetic Data Combination and Optimized SORT for
Automatic Retail Checkout,
AICity23(5391-5398)
IEEE DOI
2309
BibRef
Dhonde, A.[Anudeep],
Guntur, P.[Prabhudev],
Palani, V.[Vinitha],
Adaptive RoI with pretrained models for Automated Retail Checkout,
AICity23(5507-5510)
IEEE DOI
2309
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Vats, A.[Arpita],
Anastasiu, D.C.[David C.],
Enhancing Retail Checkout through Video Inpainting, YOLOv8 Detection,
and DeepSort Tracking,
AICity23(5530-5537)
IEEE DOI
2309
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Ghosh, P.[Pushpendu],
Wang, N.[Nancy],
Yenigalla, P.[Promod],
D-Extract: Extracting Dimensional Attributes From Product Images,
WACV23(3630-3638)
IEEE DOI
2302
Computational modeling, Computer network reliability,
Transformers, Information filters, Data models,
Vision + language and/or other modalities
BibRef
Wang, J.[Jing],
Liu, J.[Jun],
Xia, Z.W.[Zhi-Wei],
Chen, P.[Peng],
Li, X.[Xin],
Chen, X.[Xiao],
Semi-supervised Labeling Model Based on Gaussian Mixture in the
Context of E-commerce Price Fraud,
ICRVC22(300-304)
IEEE DOI
2301
Interpolation, Annotations, Computational modeling, Data models,
Regulation, Fraud, Labeling, price fraud, multiple imputations, CDBN network
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Chen, F.Y.[Fang-Yi],
Zhang, H.[Han],
Li, Z.W.[Zai-Wang],
Dou, J.C.[Jia-Chen],
Mo, S.T.[Shen-Tong],
Chen, H.[Hao],
Zhang, Y.X.[Yong-Xin],
Ahmed, U.[Uzair],
Zhu, C.C.[Chen-Chen],
Savvides, M.[Marios],
Unitail: Detecting, Reading, and Matching in Retail Scene,
ECCV22(VII:705-722).
Springer DOI
2211
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Wu, J.[Junde],
Zhang, Y.[Yu],
Fu, R.[Rao],
Liu, Y.P.[Yuan-Pei],
Gao, J.[Jing],
An Efficient Person Clustering Algorithm for Open Checkout-free
Groceries,
ECCV22(XXXVIII:17-33).
Springer DOI
2211
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Bartl, V.[Vojtech],
Španhel, J.[Jakub],
Herout, A.[Adam],
PersonGONE: Image Inpainting for Automated Checkout Solution,
AICity22(3114-3122)
IEEE DOI
2210
Deep learning, Image segmentation, Image recognition,
Urban areas, Neural networks, Detectors
BibRef
Pham, L.H.[Long Hoang],
Tran, D.N.N.[Duong Nguyen-Ngoc],
Nguyen, H.H.[Huy-Hung],
Jeon, H.J.[Hyung-Joon],
Tran, T.H.P.[Tai Huu-Phuong],
Jeon, H.M.[Hyung-Min],
Jeon, J.W.[Jae Wook],
Improving Deep Learning-based Automatic Checkout System Using Image
Enhancement Techniques,
AICity23(5333-5340)
IEEE DOI
2309
BibRef
Earlier: A1, A2, A3, A5, A4, A6, A7:
DeepACO: A Robust Deep Learning-based Automatic Checkout System,
AICity22(3106-3113)
IEEE DOI
2210
Training, Image resolution, Tracking, Pipelines, Urban areas, Benchmark testing
BibRef
Shihab, M.I.H.[Md. Istiak Hossain],
Tasnim, N.[Nazia],
Zunair, H.[Hasib],
Rupty, L.K.[Labiba Kanij],
Mohammed, N.[Nabeel],
VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness
Frame Filtration for Automatic Retail Checkout,
AICity22(3182-3190)
IEEE DOI
2210
Measurement, Training, Image segmentation, Urban areas,
Video sequences, Transformers, Entropy
BibRef
Shoman, M.[Maged],
Aboah, A.[Armstrong],
Morehead, A.[Alex],
Duan, Y.[Ye],
Daud, A.[Abdulateef],
Adu-Gyamfi, Y.[Yaw],
A Region-Based Deep Learning Approach to Automated Retail Checkout,
AICity22(3209-3214)
IEEE DOI
2210
Deep learning, Training, Runtime, Shape, Urban areas, Pipelines, Reliability
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Wan, J.F.[Jun-Feng],
Qian, S.H.[Shu-Hao],
Tian, Z.H.[Zi-Han],
Zhao, Y.Y.[Yan-Yun],
An Effective Framework of Multi-Class Product Counting and
Recognition for Automated Retail Checkout,
AICity22(3281-3289)
IEEE DOI
2210
Training, Codes, Training data, Trajectory
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Pietrini, R.[Rocco],
Rossi, L.[Luca],
Mancini, A.[Adriano],
Zingaretti, P.[Primo],
Frontoni, E.[Emanuele],
Paolanti, M.[Marina],
A Deep Learning-Based System for Product Recognition in Intelligent
Retail Environment,
CIAP22(II:371-382).
Springer DOI
2205
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Greco, A.[Antonio],
Saggese, A.[Alessia],
Vento, B.[Bruno],
A Robust and Efficient Overhead People Counting System for Retail
Applications,
CIAP22(II:139-150).
Springer DOI
2205
BibRef
Mata, C.[Cristina],
Locascio, N.[Nick],
Sheikh, M.A.[Mohammed Azeem],
Kihara, K.[Kenny],
Fischetti, D.[Dan],
StandardSim: A Synthetic Dataset for Retail Environments,
CIAP22(II:65-76).
Springer DOI
2205
BibRef
He, Z.L.[Zhao-Liang],
Wang, Y.[Yuan],
Tang, C.[Chen],
Wang, Z.[Zhi],
Zhu, W.W.[Wen-Wu],
Guo, C.Y.[Chen-Yang],
Chen, Z.B.[Zhi-Bo],
AdaConfigure: Reinforcement Learning-Based Adaptive Configuration for
Video Analytics Services,
MMMod22(I:245-257).
Springer DOI
2203
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Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Surveillance Systems, Applied to Fire and Flame Detection .