전체 글 썸네일형 리스트형 Real-Time Object Detection Meets DINOv3 https://intellindust-ai-lab.github.io/projects/DEIMv2/ DEIMv2Real-Time Object Detection Meets DINOv3intellindust-ai-lab.github.io Real-Time Object Detection Meets DINOv3Shihua Huang¹⋆, Yongjie Hou¹²⋆, Longfei Liu¹⋆, Xuanlong Yu¹, Xi Shen¹†¹ Intellindust AI Lab; ² 샤먼대학교(Xiamen University)⋆ 공동 1저자(Equal Contribution);† 교신 저자(Corresponding Author)프로젝트 페이지: https://intellindust-ai-lab.github.io/proj.. 더보기 Object Detection Model Leaderboard https://leaderboard.roboflow.com/ Computer Vision Model Leaderboard | Object Detection BenchmarksCompare object detection models like YOLO, RT-DETR, and D-FINE on COCO 2017 dataset. Filter by architecture, parameters, license, and performance metrics like mAP and F1 score.leaderboard.roboflow.com 더보기 Mamba YOLO: A Simple Baseline for Object Detection with State Space Model https://arxiv.org/abs/2406.05835 Mamba YOLO: A Simple Baseline for Object Detection with State Space ModelDriven by the rapid development of deep learning technology, the YOLO series has set a new benchmark for real-time object detectors. Additionally, transformer-based structures have emerged as the most powerful solution in the field, greatly extending the marxiv.org Mamba YOLO: 상태 공간 모델(State.. 더보기 YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection https://arxiv.org/abs/2512.23273 YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time DetectionExisting Real-Time Object Detection (RTOD) methods commonly adopt YOLO-like architectures for their favorable trade-off between accuracy and speed. However, these models rely on static dense computation that applies uniform processing to all inputs, misallarxiv.org YOLO-Mas.. 더보기 LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection https://arxiv.org/abs/2406.03459 LW-DETR: A Transformer Replacement to YOLO for Real-Time DetectionIn this paper, we present a light-weight detection transformer, LW-DETR, which outperforms YOLOs for real-time object detection. The architecture is a simple stack of a ViT encoder, a projector, and a shallow DETR decoder. Our approach leverages recent advarxiv.org LW-DETR: 실시간 객체 검출을 위한 YOLO 대체 Tr.. 더보기 RF-DETR: Neural Architecture Search for Real-Time Detection Transformers https://arxiv.org/abs/2511.09554 RF-DETR: Neural Architecture Search for Real-Time Detection TransformersOpen-vocabulary detectors achieve impressive performance on COCO, but often fail to generalize to real-world datasets with out-of-distribution classes not typically found in their pre-training. Rather than simply fine-tuning a heavy-weight vision-languagearxiv.org RF-DETR: 실시간 탐지 트랜스포머를 위한 신경.. 더보기 D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement https://arxiv.org/abs/2410.13842 D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution RefinementWe introduce D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Glarxiv.org D-FINE: DETR에서 회귀(.. 더보기 Improved YOLOv7-Tiny for Object Detection Based on UAV Aerial Images https://www.mdpi.com/2079-9292/13/15/2969 Improved YOLOv7-Tiny for Object Detection Based on UAV Aerial Images | MDPIThe core task of target detection is to accurately identify and localize the object of interest from a multitude of interfering factors.www.mdpi.com UAV 항공 영상 기반 객체 탐지를 위한 개선된 YOLOv7-TinyZitong Zhang¹, Xiaolan Xie¹,* , Qiang Guo²,* , Jinfan Xu¹¹ 중국 계림이공대학교 컴퓨터과학및공학대학, Guilin 54100.. 더보기 이전 1 2 3 4 ··· 74 다음