Arc Lab.'s Blog

[업데이트 2019.08.24 14:19] 1. 논문 M2det: A single-shot object detector based on multi-level feature pyramid network Zhao, Q., Sheng, T., Wang, Y., Tang, Z., Chen, Y., Cai, L., & Ling, H. (2019). M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9259-9266. https://doi.org/10.1609/aaai.v33i01.330192..

[업데이트 2019.08.24 14:26] 1. 논문 SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network Yancheng Bai, Yongqiang Zhang, Mingli Ding, Bernard Ghanem; The European Conference on Computer Vision (ECCV), 2018, pp. 206-221 2. 요약 - To deal with the small object detection problem, we propose an end-to-end multi-task generative adversarial network (MTGAN). In the MTGAN, the generato..

Panoptic feature pyramid networks

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 년도별 TT100K 데이터셋 사용한 논문들(구글 스칼라 검색) 2019(9건) -Traffic sign detection and recognition based on pyramidal convolutional networks -Variational Prototyping-Encoder: One-Shot L..

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분 - Page 1 • Experiments on the German Traffic Sign Detection Benchmark (GTSDB) [9] and the Tsinghua-Tencent 100K (TT-100K) data set [10] show that the proposed appro..

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분 4.1. Architectural design strategies Our overarching purpose is to build a CNN model with few model pa- rameters but a competitive detection accuracy. To this end, ..

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분 - Page 1~2 In 2016, Zhu et al. [16] proposed the Tsinghua-Tencent 100K dataset, the largest and most challenging traffic sign dataset. The Tsinghua-Tencent 100K dat..

1. 인용 논문 Traffic-sign detection and classification in the wild Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2110-2118 2. 인용 부분