NISSO

Focal Loss for Dense Object Detection (2017) https://arxiv.org/abs/1708.02002 Focal Loss for Dense Object Detection The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampl arxiv.org RetinaNet은 Clas..

YOLO:: You Only Look Once:Unified, Real-Time Object Detection (2016) https://arxiv.org/abs/1506.02640 You Only Look Once: Unified, Real-Time Object Detection We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated..

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks https://arxiv.org/abs/1506.01497 Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks,..

Very Deep Convolutional Networks for Large-Scale Image Recognition * 대규모 이미지 인식을 위한 아주 깊은 컨볼루션 신경망 * Computer Vision 분야에 대한 큰 가능성을 보여줌 * VGG : Visual Geometry Group, 옥스포드대 연구팀 이름 https://arxiv.org/pdf/1409.1556.pdf VGGNet은 CNN의 깊이에 따른 정확도를 연구할 목적으로 만든 이미지 인식 모델이다. Abstract 대규모 이미지 인식에서 convolutional network의 깊이가 정확도에 미치는 영향을 조사 3*3 convolution filter 구조를 사용해 깊은 신경망을 평가 16-19 weight layer의 깊이로 선행..