https://github.com/LourencoVazPato/seeing-without-looking
공식 코드Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization
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/Lucia-Ningning/Adaptive_Fusion_RGBD_Saliency_Detection
공식 코드https://github.com/Lucs1590/Coffee_Recognize
공식 코드Artificial intelligence for detection and quantification of rust and leaf miner in coffee crop
https://github.com/Lx-Bao/QSFNet
공식 코드Quality-aware Selective Fusion Network for V-D-T Salient Object Detection
https://github.com/M4gicT0/autonomous-drone-racing
공식 코드Image Generation for Efficient Neural Network Training in Autonomous Drone Racing
https://github.com/MCG-NJU/MOTIP
공식 코드https://github.com/MECLabTUDA/VoxelSceneGraph
공식 코드https://github.com/MIC-DKFZ/nnDetection
공식 코드nnDetection: A Self-configuring Method for Medical Object Detection
https://github.com/MIPT-Oulu/DeepWrist
공식 코드Critical Evaluation of Deep Neural Networks for Wrist Fracture Detection
https://github.com/MIT-HAN-LAB/temporal-shift-module
공식 코드TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/MIT-SPARK/TEASER-plusplus
공식 코드https://github.com/MIVRC/MDCN-PyTorch
공식 코드MDCN: Multi-scale Dense Cross Network for Image Super-Resolution
https://github.com/MIVRC/SSRDEFNet-PyTorch
공식 코드Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
https://github.com/MKFMIKU/SrSENet
공식 코드An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks
https://github.com/MMehdiMousavi/SuperCaustics
공식 코드https://github.com/Maclory/SPSR
공식 코드Structure-Preserving Super Resolution with Gradient Guidance
https://github.com/Madave94/multi-annotator-toolkit
공식 코드https://github.com/Madhu081096/Reference-based-MRI-superresolution-using-texture-transfer
공식 코드Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images
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/MandyZChen/srez
공식 코드https://github.com/Manjuphoenix/IndraEye
공식 코드IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks
https://github.com/ManuelFritsche/real-world-sr
공식 코드https://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/MclTTI/RainScaleGAN
공식 코드RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.