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2D Classification
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JingZhang617/COD-Rank-Localize-and-Segment
공식 코드Simultaneously Localize, Segment and Rank the Camouflaged Objects
https://github.com/JingZhang617/Joint_COD_SOD
공식 코드Uncertainty-aware Joint Salient Object and Camouflaged Object Detection
https://github.com/JingZhang617/Scribble_Saliency
공식 코드Weakly-Supervised Salient Object Detection via Scribble Annotations
https://github.com/JingZhang617/UCNet
공식 코드https://github.com/JingbiaoMei/BayesFTNA
공식 코드BayesFT: Bayesian Optimization for Fault Tolerant Neural Network Architecture
https://github.com/Jinming-Su/PurNet
공식 코드Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss
https://github.com/Jinming-Su/TSOD
공식 코드Exploring Driving-aware Salient Object Detection via Knowledge Transfer
https://github.com/Jittor/JDet
공식 코드https://github.com/JoeRoussy/adaptive-attention-in-cv
공식 코드https://github.com/Johann-Huber/qd_grasp
공식 코드Toward a Plug-and-Play Vision-Based Grasping Module for Robotics
https://github.com/Johnathan-Xie/ZSD-YOLO
공식 코드Zero-shot Object Detection Through Vision-Language Embedding Alignment
https://github.com/JonnoB/clocrc
공식 코드CLOCR-C: Context Leveraging OCR Correction with Pre-trained Language Models
https://github.com/JonnoB/scrambledtext
공식 코드Scrambled text: training Language Models to correct OCR errors using synthetic data
https://github.com/JonnoB/scrambledtext_analysis
공식 코드Scrambled text: training Language Models to correct OCR errors using synthetic data
https://github.com/JonnoB/training_lms_with_synthetic_data
공식 코드Scrambled text: training Language Models to correct OCR errors using synthetic data
https://github.com/JosephChenHub/DPANet
공식 코드DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection
https://github.com/JosephChenHub/GCPANet
공식 코드Global Context-Aware Progressive Aggregation Network for Salient Object Detection
https://github.com/JosephKJ/OWOD
공식 코드https://github.com/JosephKJ/iOD
공식 코드https://github.com/Js-Mim/rl_singing_voice
공식 코드Unsupervised Interpretable Representation Learning for Singing Voice Separation
https://github.com/JuanFMontesinos/Acappella-YNet
공식 코드https://github.com/JudasDie/SOTS
공식 코드https://github.com/JulySinceAndrew/FedNewsRec-EMNLP-Findings-2020
공식 코드https://github.com/Jumponthemoon/WeatherGS
공식 코드WeatherGS: 3D Scene Reconstruction in Adverse Weather Conditions via Gaussian Splatting
https://github.com/Jun-Pu/UCOS-DA
공식 코드Unsupervised Camouflaged Object Segmentation as Domain Adaptation
https://github.com/KIT-MRT/YOLinO
공식 코드https://github.com/KPeng9510/MASS
공식 코드MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding
https://github.com/KU-CVLAB/INR-st
공식 코드Controllable Style Transfer via Test-time Training of Implicit Neural Representation
https://github.com/KU-CVLAB/Perturbed-Attention-Guidance
공식 코드Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
https://github.com/KaimingHe/resnet-1k-layers
공식 코드https://github.com/KaiyangZhou/Dassl.pytorch
공식 코드MixStyle Neural Networks for Domain Generalization and Adaptation
https://github.com/KaiyangZhou/mixstyle-release
공식 코드MixStyle Neural Networks for Domain Generalization and Adaptation
https://github.com/KaiyuYue/cgnl-network.pytorch
공식 코드https://github.com/KaiyuanZh/FLIP
공식 코드FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
https://github.com/KangchengLiu/FAC_Foreground_Aware_Contrast
공식 코드Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.