Papers with Code 아카이브
Object Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MadryLab/cifar10_challenge
공식 코드Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/MadryLab/mnist_challenge
공식 코드Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/MahmudulHasan11085/Vision-Eagle-Attention
공식 코드Vision Eagle Attention: a new lens for advancing image classification
https://github.com/MahyarNajibi/SNIPER
공식 코드https://github.com/MalongTech/research-fad
공식 코드Representation Sharing for Fast Object Detector Search and Beyond
https://github.com/Manjuphoenix/IndraEye
공식 코드IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks
https://github.com/MartinHahner/LiDAR_fog_sim
공식 코드Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather
https://github.com/MasterBin-IIAU/UNINEXT
공식 코드Universal Instance Perception as Object Discovery and Retrieval
https://github.com/MasterHow/CoBEV
공식 코드CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity
https://github.com/MathLee/CMWNet
공식 코드Cross-Modal Weighting Network for RGB-D Salient Object Detection
https://github.com/MathLee/ICNet-for-RGBD-SOD
공식 코드https://github.com/Mazin-Hnewa/MS-DAYOLO
공식 코드Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection
https://github.com/MedChaabane/DEFT
공식 코드https://github.com/Media-Smart/vedadet/tree/main/configs/trainval/tinaface
공식 코드https://github.com/Megvii-BaseDetection/AutoAssign
공식 코드AutoAssign: Differentiable Label Assignment for Dense Object Detection
https://github.com/Megvii-BaseDetection/BorderDet
공식 코드https://github.com/Megvii-BaseDetection/DeFCN
공식 코드End-to-End Object Detection with Fully Convolutional Network
https://github.com/Megvii-BaseDetection/DisAlign
공식 코드Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
https://github.com/Megvii-BaseDetection/GFSD
공식 코드https://github.com/Megvii-BaseDetection/LLA
공식 코드LLA: Loss-aware Label Assignment for Dense Pedestrian Detection
https://github.com/Megvii-BaseDetection/OTA
공식 코드https://github.com/Megvii-BaseDetection/YOLOX
공식 코드https://github.com/Megvii-Nanjing/CR-DA-DET
공식 코드Exploring Categorical Regularization for Domain Adaptive Object Detection
https://github.com/MegviiDetection/FSCE
공식 코드FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
https://github.com/Mehrdad-Noori/CAGNet
공식 코드https://github.com/Mehrdad-Noori/Saliency-Evaluation-Toolbox
공식 코드https://github.com/Menace-Dragon/VP-QOD
공식 코드https://github.com/MengLcool/RichSem
공식 코드Learning from Rich Semantics and Coarse Locations for Long-tailed Object Detection
https://github.com/MenghaoGuo/-EANet
공식 코드Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks
https://github.com/MenghaoGuo/Awesome-Vision-Attentions
공식 코드https://github.com/MhLiao/TextBoxes_plusplus
공식 코드https://github.com/Mhaiyang/CVPR2021_PFNet
공식 코드https://github.com/MichaelFan01/STDC-Seg
공식 코드https://github.com/Microsoft/human-pose-estimation.pytorch
공식 코드Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/MikeS96/3d_obj_detection
공식 코드High-level camera-LiDAR fusion for 3D object detection with machine learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.