ICCV 2019 Review

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MYEONGGYU LEE

review date: 2019/11/04 (by Meyong-Gyu.LEE @Soongsil Univ.) Kor review of ICCV 2019.

Technology

CGLAB 이명규ICCV 2019 REVIEW (1/9) Presented by CGLAB 이명규
ICCV 2019 REVIEW

CGLAB 이명규ICCV 2019 REVIEW (2/9)
INDEX
1. Conference Introduction
2. ICCV 2019 Statistics
3. Trends of ICCV 2019

CGLAB 이명규ICCV 2019 REVIEW (3/9)
↳
Conference Introduction1-1
• International Conference on Computer Vision
• 격년제로 개최, CVPR과 함께 컴퓨터과학 분야 최고 수준 학회 중 하나
• 컴퓨터 비전 및 인공지능, 패턴인식 주제로 올해는 Oct. 27 ~ Nov.2 개최
• 결성일 : 1987년, 평균 accept rate : 26%
• 역대 개최지 : 서울(2019), 베니스(2017), 산티아고(2015), ….
ICCV 2019

CGLAB 이명규ICCV 2019 REVIEW (4/9)
↳
• 3D Computer Vision, Action Recognition, Biometrics, face and gesture,
Biomedical image analysis, Computational photography, photometry,
shape from X, Deep Learning, Low-level vision and Image Processing,
Motion and Tracking, Optimization methods
• Recognition: detection, categorization, indexing and matching, Robot
Vision, Segmentation, grouping and shape representation, Statistical
learning
Call For Papers
Conference Introduction1-1

CGLAB 이명규ICCV 2019 REVIEW (5/9)
↳
Acceptance Rate
ICCV 2019 Statistics1-2
27.90%
30.90%
29.00%
25.00%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
ICCV'13 ICCV'15 ICCV'17 ICCV'19
Acceptance Rate
Acceptance Rate
1629 1698
2143
4304
454 525 621
1077
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
ICCV'13 ICCV'15 ICCV'17 ICCV'19
Submitted/Accepted Paper
Submitted Accepted

CGLAB 이명규ICCV 2019 REVIEW (6/9)
↳
Top 30 Keywords
Trends of ICCV 20191-3
https://hoya012.github.io/blog/ICCV-2019-paper-preview/
Graph↑
Adversarial↑
Unsupervised↑
Attention↑
Re-Identification↑

CGLAB 이명규ICCV 2019 REVIEW (7/9)
↳ Other Keywords….
• Scene Understanding
➢Scene Graph
• Graph Neural Network
• Network Architecture Search
➢Curriculum learning, RL
• Lightweight Model
➢Quantization(Model Compression)
• Few-Shot Learning
➢Metric Learning : Tuple loss, pair loss etc
➢Meta Learning
Trends of ICCV 20191-3
• Semantic Adversarial Attack
• Knowledge Distillation
• Super Resolution
➢SR in Low-light environment
➢SR in Video
• Pose Estimation
➢Using Point Cloud
➢Using Single RGB Image
• Object Detection & Segmentation
➢Single image or Video

CGLAB 이명규ICCV 2019 REVIEW (8/9)
↳ Other Keywords….
• Scene Graph
https://cs.stanford.edu/people/ranjaykrishna/sgrl/index.html
• Graph Neural Network
https://tykimos.github.io/warehouse/2019-7-4-ISS_2nd_Deep_Learning_Conference_All_Together_jtkim_file.pdf
https://www.slideshare.net/JungwonKim10/graph-neural-network
• Few-Shot Learning
https://brstar96.github.io/posttranslation/Few-Shot-Learning-in-CVPR-2019/
• Knowledge Distillation
https://blog.lunit.io/2018/03/22/distilling-the-knowledge-in-a-neural-network-nips-2014-workshop/
https://jamiekang.github.io/2017/05/21/distilling-the-knowledge-in-a-neural-network/
• Lightweight model – Quantization
http://blog.naver.com/PostView.nhn?blogId=qualcommkr&logNo=221522181807&parentCategoryNo=&categoryNo=&viewDate=&isSh
owPopularPosts=false&from=menu
Trends of ICCV 20191-3

CGLAB 이명규ICCV 2019 REVIEW (9/9)
Thank you for Listening.
Email : brstar96@naver.com (or brstar96@soongsil.ac.kr)
Mobile : +82-10-8234-3179

The Intelligent Edge computing paradigm is playing a major role in the design and development of Cyber-Physical and Cloud Systems (CPCS), extending the Cloud and overcoming its limitations so as to better address the issues related with the physical dimension of data—and therefore of the data-aware intelligence (such as context-awareness and real-time responses). Despite the proliferation of research works in this area, a well-founded software engineering approach specifically addressing the distribution of intelligence sources between the Edge and the Cloud is still missing. In this paper we propose some general criteria along with a coherent set of guidelines to follow in the design of distributed intelligence within CPCS, suitably exploiting Edge and Cloud paradigms to effectively enable data intelligence and accounting for both symbolic and sub-symbolic approaches to reasoning. Then, we exploit the notion of micro-intelligence as situated intelligence for Edge computing, promoting the idea of intelligent environment embodying rational processes meant to complement the cognitive process of individuals in order to reduce their cognitive workload and augment their cognitive capabilities. In order to demonstrate the general applicability of our guidelines, we propose Situated Logic Programming (SLP) as the conceptual frame- work for delivering micro-intelligence in CPCS, and Logic Programming as a Service (LPaaS) as its reference architecture and technological embodiment.

Traffic Congestion Detection Using Deep Learning

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Despite the huge amount of traffic surveillance videos and images have been accumulated in the daily monitoring, deep learning approaches have been underutilized in the application of traffic intelligent management and control. Traffic images, including various illumination, weather conditions, and vast scenarios are considered and preprocessed to set up a proper training dataset. In order to detect traffic congestion, a network structure is proposed based on residual learning to be pre trained and fine tuned. The network is then transferred to the traffic application and retrained with self established training dataset to generate the Traffic Net. The accuracy of Traffic Net to classify congested and uncongested road states reaches 99 for the validation dataset and 95 for the testing dataset. The trained model is stored in cloud storage for easy access for application from anywhere. The proposed Traffic Net can be used by a regional detection of traffic congestion on a large scale surveillance system. The effectiveness and efficiencies are magnificently demonstrated with quick detection in the high accuracy in the case study. The experimental trial could extend its successful application to traffic surveillance system and has potential enhancement for intelligent transport system in future. Anusha C | Dr. J. Bhuvana "Traffic Congestion Detection Using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49401.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/49401/traffic-congestion-detection-using-deep-learning/anusha-c

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ICCV 2019 Review

1. CGLAB 이명규ICCV 2019 REVIEW (1/9) Presented by CGLAB 이명규 ICCV 2019 REVIEW

2. CGLAB 이명규ICCV 2019 REVIEW (2/9) INDEX 1. Conference Introduction 2. ICCV 2019 Statistics 3. Trends of ICCV 2019

3. CGLAB 이명규ICCV 2019 REVIEW (3/9) ↳ Conference Introduction1-1 • International Conference on Computer Vision • 격년제로 개최, CVPR과 함께 컴퓨터과학 분야 최고 수준 학회 중 하나 • 컴퓨터 비전 및 인공지능, 패턴인식 주제로 올해는 Oct. 27 ~ Nov.2 개최 • 결성일 : 1987년, 평균 accept rate : 26% • 역대 개최지 : 서울(2019), 베니스(2017), 산티아고(2015), …. ICCV 2019

4. CGLAB 이명규ICCV 2019 REVIEW (4/9) ↳ • 3D Computer Vision, Action Recognition, Biometrics, face and gesture, Biomedical image analysis, Computational photography, photometry, shape from X, Deep Learning, Low-level vision and Image Processing, Motion and Tracking, Optimization methods • Recognition: detection, categorization, indexing and matching, Robot Vision, Segmentation, grouping and shape representation, Statistical learning Call For Papers Conference Introduction1-1

5. CGLAB 이명규ICCV 2019 REVIEW (5/9) ↳ Acceptance Rate ICCV 2019 Statistics1-2 27.90% 30.90% 29.00% 25.00% 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% ICCV'13 ICCV'15 ICCV'17 ICCV'19 Acceptance Rate Acceptance Rate 1629 1698 2143 4304 454 525 621 1077 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 ICCV'13 ICCV'15 ICCV'17 ICCV'19 Submitted/Accepted Paper Submitted Accepted

6. CGLAB 이명규ICCV 2019 REVIEW (6/9) ↳ Top 30 Keywords Trends of ICCV 20191-3 https://hoya012.github.io/blog/ICCV-2019-paper-preview/ Graph↑ Adversarial↑ Unsupervised↑ Attention↑ Re-Identification↑

7. CGLAB 이명규ICCV 2019 REVIEW (7/9) ↳ Other Keywords…. • Scene Understanding ➢Scene Graph • Graph Neural Network • Network Architecture Search ➢Curriculum learning, RL • Lightweight Model ➢Quantization(Model Compression) • Few-Shot Learning ➢Metric Learning : Tuple loss, pair loss etc ➢Meta Learning Trends of ICCV 20191-3 • Semantic Adversarial Attack • Knowledge Distillation • Super Resolution ➢SR in Low-light environment ➢SR in Video • Pose Estimation ➢Using Point Cloud ➢Using Single RGB Image • Object Detection & Segmentation ➢Single image or Video

8. CGLAB 이명규ICCV 2019 REVIEW (8/9) ↳ Other Keywords…. • Scene Graph https://cs.stanford.edu/people/ranjaykrishna/sgrl/index.html • Graph Neural Network https://tykimos.github.io/warehouse/2019-7-4-ISS_2nd_Deep_Learning_Conference_All_Together_jtkim_file.pdf https://www.slideshare.net/JungwonKim10/graph-neural-network • Few-Shot Learning https://brstar96.github.io/posttranslation/Few-Shot-Learning-in-CVPR-2019/ • Knowledge Distillation https://blog.lunit.io/2018/03/22/distilling-the-knowledge-in-a-neural-network-nips-2014-workshop/ https://jamiekang.github.io/2017/05/21/distilling-the-knowledge-in-a-neural-network/ • Lightweight model – Quantization http://blog.naver.com/PostView.nhn?blogId=qualcommkr&logNo=221522181807&parentCategoryNo=&categoryNo=&viewDate=&isSh owPopularPosts=false&from=menu Trends of ICCV 20191-3

9. CGLAB 이명규ICCV 2019 REVIEW (9/9) Thank you for Listening. Email : brstar96@naver.com (or brstar96@soongsil.ac.kr) Mobile : +82-10-8234-3179

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