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CVDL 2024: International Conference on Computer Vision and Deep Learning

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CVDL
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투고 마감일:
2023-12-10
통보일:
개최일:
2024-01-20
개최지:
Changsha, China
조회: 8613   팔로우: 1   참가: 1

논문 모집

CVDL 2024 (International Conference on Computer Vision and Deep Learning) is an academic conference held in Changsha, China on 2024-01-20. The paper submission deadline is 2023-12-10.

You are kindly encouraged to contribute to and help shape the conference through submissions of your research abstracts and papers to CVDL 2024 conference. Topics of interest for submission include: Track 1: Computer Vision Big data and computer vision Biomedical image analysis Image and video coding Image and video retrieval Remote sensing image Computational photography Optimization and method of study Sensor and display Data collection and performance analysis Computer vision of deep learning Document image analysis Character identification Analysis, behavior identity Visual models Video analysis Multimodal information processing Visual and language Motion and tracking 3D reconstruction Human-computer interaction Track 2: Deep Learning Machine Learning Unsupervised Feature Learning Deep Reinforcement Learning Convolutional Neural Networks (CNN) Deep Hierarchical Networks (DHN) Recurrent Neural Network (RNN) Neuro-Fuzzy Algorithms Autoencoders Graph Representation Learning Clustering, Classification and Regression Evolutionary Methods Image Processing Autonomic Computing User Modeling Information Retrieval and Reuse Question-Answering Language Translation Cognitive Architectures Recommender Systems
최종 수정: Dou Sun ()

관련 학회

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems6.1Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536
AIEEE Transactions on Dependable and Secure Computing7.5IEEE1545-5971

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