Explore machine learning papers and reviews related to Object Detection. Find insights, analysis, and implementation details.
Bin Xiao, Haiping Wu, +7
How Florence-2 turns detection, segmentation, grounding, captioning, and OCR into one sequence-to-sequence problem driven by task prompts, expressing spatial outputs as location tokens in the text vocabulary.
Yanghao Li, Hanzi Mao, +2
Investigating the effectiveness of plain Vision Transformers as backbones for object detection and proposing modifications to improve their performance.
Joseph Redmon, Santosh Divvala, +2
Introducing YOLO, a unified, real-time object detection system that frames object detection as a single regression problem.
Shaoqing Ren, Kaiming He, +2
Faster R-CNN explained: how Region Proposal Networks (RPN) enable near real-time object detection with shared convolutional features.
Nicolas Carion, Francisco Massa, +4
Introducing DETR, a novel end-to-end object detection framework that leverages Transformers to directly predict a set of object bounding boxes.
Ze Liu, Yutong Lin, +6
Swin Transformer: hierarchical Vision Transformer using shifted windows for efficient image classification, object detection, and segmentation.