Computer Vision Deep Learning's Paper Review By Seunghwan

1. BackBone

(1-1) ResNet

[Deep Residual Learning for Image Recognition]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, 2015 (Microsoft Research)

• review : https://ropiens.tistory.com/32?category=896592

• paper : https://arxiv.org/pdf/1512.03385.pdf

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2. Object Detection

(2-1) R-CNN

[Region-based Convolutional Networks for Accurate Object Detection and Segmentation]

Ross Girshick, Jeff Donahue, Jitendra Malik, 2015 (IEEE)

• review : https://ropiens.tistory.com/73

• paper : http://islab.ulsan.ac.kr/files/announcement/513/rcnn_pami.pdf

(2-2) YOLOv3

[YOLOv3: An Incremental Improvement ]

Joseph Redmon, Ali Farhadi

• review : https://ropiens.tistory.com/43?category=896592

• paper : https://arxiv.org/pdf/1804.02767.pdf

(2-3) YOLOv4

[YOLOv4: Optimal Speed and Accuracy of Object Detection]

Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao

• review : https://ropiens.tistory.com/33?category=896592

• paper : https://arxiv.org/pdf/2004.10934.pdf

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3. Character recognition

(3-1) TPS-ResNet-BiLSTM-Attn

[What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis]

Jeonghun Baek, Geewook Kim, Junyeop Lee, Sungrae Park, Dongyoon Han, Sangdoo Yun, Seong Joon Oh, Hwalsuk Lee, 2019 (Clova AI Research, NAVER/LINE Corp, Kyoto University)

• review : https://ropiens.tistory.com/23?category=896592

• paper : https://arxiv.org/pdf/1904.01906.pdf