Arc Lab.'s Blog

[업데이트 2019.09.04 15:36] 1. 논문 Attention U-Net: Learning Where to Look for the Pancreas Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert(Submitted on 11 Apr 2018 (v1), last revised 20 May 2018 (this version, v3)) https://arxiv.org/abs/1804.03999 - GitHub: https://g..

[업데이트 2019.09.04 11:17] 1. 논문 Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels Medical Image Analysis 53 (2019) 197–207 Article history: Received 15 August 2018 Revised 15 January 2019 Accepted 18 January 2019 Available online 5 February 2019 https://www.sciencedirect.com/science/article/pii/S1361841518306133 2. 요약 - We propose a novel attention gate (AG) mode..

[업데이트 2019.09.03 21:03] 1. 논문 Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels Martin Zlocha, Qi Dou, Ben Glocker (Submitted on 5 Jun 2019) Comments: Accepted at MICCAI 2019 Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:1906.02283 [eess.IV] (or arXiv:1906.02283v1 [eess.I..

[업데이트 2019.08.24 14:15] 1. 논문 Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network Remote Sens. 2019, 11(7), 755; https://doi.org/10.3390/rs11070755 Received: 17 February 2019 / Revised: 20 March 2019 / Accepted: 23 March 2019 / Published: 28 March 2019 2. 요약 - To solve these problems, an effective region-based VHR remote sensi..

[업데이트 2019.08.24 01:29] 1. 논문 Efficient Featurized Image Pyramid Network for Single Shot Detector CVPR 2019 2. 요약 - To detect very small/large objects, classical pyramid representation can be exploited, where an image pyramid is used to build a feature pyramid (featurized image pyramid), enabling detection across a range of scales. - In this paper, we introduce a light-weight architecture to eff..

[업데이트 2019.08.24 01:16] 1. 논문 Improving Object Detection from Scratch via Gated Feature Reuse 2. 요약 - In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD. - We call our module GFR (Gated Feature Reuse), which exhibits two main advantages. First, we introduce a novel gate-controlled prediction strategy enabled by Squeeze-and-Excitation..

[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..