论文阅读笔记

• MultiModal Machine Learning
  • BLIP: Bootstrapping Language-Image Pre-training
  • BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
• Large Language Models
  • OPT: OPT : Open Pre-trained Transformer Language Models
  • GPT-v1:Improving Language Understanding by Generative Pre-Training
  • GPT-v2:Language Models are Unsupervised Multitask Learners
  • GPT-v3:Language Models are Few-Shot Learners
  • GPT-v4:GPT-4 Technical Report
• Object Detection
  • summary
  • RCNN
  • Fast R-CNN
  • Faster R-CNN
  • Mask R-CNN
  • FCN
  • R-FCN
  • FPN
  • FCOS
  • SSD
  • Mobilenet-SSDv2
  • 论文阅读笔记
  • 回答问题
  • OneNet
  • Mask R-CNN
  • Cascade-RCNN
  • RetinaNet
  • FemtoDet
  • SparseInst
  • YOLOv1
  • YOLOv2
  • YOLOv3
  • YOLOv4
  • Scaled-YOLOv4
  • Edge-YOLO
  • MS-DAYOLO
  • ASFF
  • ATSS
  • SABL
  • SM-NAS
  • TSD
  • RDSNet
  • CenterMask
  • EfficientDet
  • Simple Multi-dataset Detection
  • YOLOX
  • YOLOv6
  • PP-YOLOv1
  • PP-YOLOv2
  • PP-YOLOE
  • YOLOF
  • YOLOP
  • YOLOR
  • YOLOS
  • YOLOv7
  • DY-yolov7
  • Gold-YOLO
  • YOLOv6
  • DAMO-YOLO
  • ViT-YOLO
  • YOLO-MS
  • DETR
  • RT-DETR
  • YOLOv9
  • YOLOOC
  • FemtoDet
  • MS-DAYOLO
  • OneNet
  • Sparse R-CNN
  • SparseInst
  • OWL-ViT
  • OWLv2
  • RTMDet
  • YOLO-World
  • YOLOOC
  • MDETR
  • YOLOv10
  • 目标检测二十年：一项综述
  • 1 引言
  • 2 目标检测二十年
  • 4 最近目标检测的进展
  • 5 应用
  • 6 结论和未来方向
  • YOLO的全面综述：从YOLOv1到YOLOv8及未来
  • 摘要
  • 1 引言
  • 2 YOLO在不同领域的应用
  • 3 目标检测指标和非最大值抑制（NMS）
  • 4 YOLO：只看一次
  • 5 YOLOv2：更好、更快、更强
  • 6 YOLOv3
  • 7 骨干、颈部和头部
  • 8 YOLOv4
  • 10 Scaled-YOLOv4
  • 11 YOLOR
  • 12 YOLOX
  • 13 YOLOv6
  • 13.1 YOLOv6 结果
  • 14 YOLOv7
  • 15 DAMO-YOLO
  • 16 YOLOv8
  • 18 讨论
  • 19 YOLO 的未来
  • 20.1 速度和准确性之间的权衡