Papers with Code 아카이브
Object Detection
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/VRI-UFPR/IDA
공식 코드IDA: Improved Data Augmentation Applied to Salient Object Detection
https://github.com/Vegeta2020/CIA-SSD
공식 코드CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud
https://github.com/Vegeta2020/SE-SSD
공식 코드SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud
https://github.com/VerwimpEli/CLAD
공식 코드CLAD: A realistic Continual Learning benchmark for Autonomous Driving
https://github.com/VimsLab/SODAWideNet
공식 코드https://github.com/VisDrone/DroneCrowd
공식 코드Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark
https://github.com/VisDrone/VisDrone-Dataset
공식 코드https://github.com/Visual-Attention-Network/VAN-Classification
공식 코드https://github.com/VisualAIKHU/SAMPD
공식 코드Multispectral Pedestrian Detection with Sparsely Annotated Label
https://github.com/Vladimir2506/Pointformer
공식 코드https://github.com/WISION-Lab/event-nn-pytorch
공식 코드https://github.com/WISION-Lab/event-nn-tf
공식 코드https://github.com/WISION-Lab/eventful-transformer
공식 코드Eventful Transformers: Leveraging Temporal Redundancy in Vision Transformers
https://github.com/WXinlong/ForeSeE
공식 코드Task-Aware Monocular Depth Estimation for 3D Object Detection
https://github.com/WXinlong/SOLO
공식 코드https://github.com/WYFDUT/MonoASRH
공식 코드Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection
https://github.com/WanXiaopei/aadi
공식 코드https://github.com/WangLibo1995/GeoSeg
공식 코드https://github.com/WangYueFt/pillar-od
공식 코드https://github.com/Wangjing1551/LogoDet-3K-Dataset
공식 코드https://github.com/WangyiNTU/Point-supervised-crowd-detection
공식 코드A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds
https://github.com/WaterScenes/USVTrack-Baseline-YOLO-Tracking
공식 코드https://github.com/WeiZongqi/CG-Net
공식 코드Learning Calibrated-Guidance for Object Detection in Aerial Images
https://github.com/WeijingShi/Point-GNN
공식 코드Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
https://github.com/WenqiangX/ese_seg
공식 코드https://github.com/Westlake-AI/A2MIM
공식 코드Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN
https://github.com/Westlake-AI/MogaNet
공식 코드https://github.com/Westlake-AI/openmixup
공식 코드Exploring Localization for Self-supervised Fine-grained Contrastive Learning
https://github.com/WinFrand/MELM
공식 코드Min-Entropy Latent Model for Weakly Supervised Object Detection
https://github.com/Windere/snn-cvt-dual-phase
공식 코드Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization
https://github.com/Winfrand/C-MIL
공식 코드C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
https://github.com/WongKinYiu/CrossStagePartialNetworks
공식 코드CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/WongKinYiu/ScaledYOLOv4
공식 코드https://github.com/WongKinYiu/YOLO
공식 코드YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
https://github.com/WongKinYiu/yolor
공식 코드You Only Learn One Representation: Unified Network for Multiple Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.