[업데이트 2019.08.23 18:45] 1. 논문 Multi-Attention Object Detection Model in Remote Sensing Images Based on Multi-Scale IEEE Access SPECIAL SECTION ON DATA MINING FOR INTERNET OF THINGS Received June 27, 2019, accepted July 8, 2019, date of publication July 15, 2019, date of current version July 31, 2019. 2. 요약 INDEX TERMS : Object detection, satellite imagery, pixel-level attention, spatial attentio..
[업데이트 2019.08.24 12:22] 1. 논문 Cascade R-CNN: High Quality Object Detection and Instance Segmentation arXiv:1906.09756v1 [cs.CV] 24 Jun 2019 2. 요약 Index Terms—Object Detection, High Quality, Cascade, Bounding Box Regression, Instance Segmentation. - A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds - The obse..
[업데이트 2019.08.24 13:14] 1. 논문 Libra r-cnn: Towards balanced learning for object detection The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 821-830 2019.07.16-20 soure code: https://github.com/OceanPang/Libra_R-CNN 2. 요약 - In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the ..
[업데이트 2019.08.24 13:59] 1. 논문 Nas-fpn: Learning scalable feature pyramid architecture for object detection Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 7036-7045 2019.06.16-20 2. 요약 - Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and disco..
[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..
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