NMS3 [X:AI] RetinaNet 논문 리뷰 논문 원본 : https://arxiv.org/abs/1708.02002 Focal Loss for Dense Object DetectionThe 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 samplarxiv.org 1. Abstract & Introduction본 논문 시점 기준 Object Detection .. 2025. 2. 3. [X:AI] YOLO 논문 리뷰 논문 원본 : https://arxiv.org/abs/1506.02640 You Only Look Once: Unified, Real-Time Object DetectionWe 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 class probabiliarxiv.org Abstract이전 연구들은 객체 탐지를 위해 분류 모델을 변형.. 2025. 1. 28. [X:AI] Faster-RCNN 논문 리뷰 논문 원본 : https://arxiv.org/abs/1506.01497 Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksState-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, exposing region proposal computation as a bottlearxiv.org 1. Abstract .. 2025. 1. 21. 이전 1 다음
