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CVDL 2024: International Conference on Computer Vision and Deep Learning

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Submission Date:
2023-12-10
Notification Date:
Conference Date:
2024-01-20
Location:
Changsha, China
Viewed: 8950   Tracked: 1   Attend: 1

Conference Partner Index (CP-I)

42.7 / 100
Ranked #3,827 of 5,682 conferences · Top 68%

#195 of 244 in Computer Vision & Pattern Recognition #488 of 739 in Artificial Intelligence & Machine Learning

Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
15
Public record completeness (15%)
25

Inputs used: Researchers following it here: 1 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 25% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-19

Call For Papers

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
Last updated by Dou Sun on

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