https://github.com/WYFDUT/MonoASRH
공식 코드Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection
Papers with Code 아카이브
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/Wenri/DTSN
공식 코드Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical Rectification
https://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
https://github.com/WoojuLee24/OA-DG
공식 코드https://github.com/Wuziyi616/LEOD
공식 코드https://github.com/XJTU-Haolin/TT3D
공식 코드https://github.com/XLearning-SCU/2025-CVPR-MaIR
공식 코드MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration
https://github.com/XY-boy/FreMamba
공식 코드Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution
https://github.com/XYLGroup/DKP
공식 코드A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
https://github.com/XavierYoungY/SALT-Net
공식 코드Rethinking the Misalignment Problem in Dense Object Detection
https://github.com/Xiangyu-Gao/Radar_multiple_perspective_object_detection
공식 코드RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition
https://github.com/Xianpeng919/MonoCon
공식 코드Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection
https://github.com/Xiaoqi-Zhao-DLUT/DANet-RGBD-Saliency
공식 코드A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection
https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency
공식 코드Suppress and Balance: A Simple Gated Network for Salient Object Detection
https://github.com/XieQi2015/F-Conv
공식 코드Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions
https://github.com/XingangPan/IBN-Net
공식 코드Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/XingangPan/Switchable-Whitening
공식 코드https://github.com/XinyiYing/RGBT-Tiny
공식 코드Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.