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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/NVlabs/BundleSDF
공식 코드BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
https://github.com/NVlabs/CenterPose
공식 코드Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image
https://github.com/NVlabs/DICOD
공식 코드https://github.com/NVlabs/FasterViT
공식 코드FasterViT: Fast Vision Transformers with Hierarchical Attention
https://github.com/NVlabs/FocalFormer3D
공식 코드FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
https://github.com/NVlabs/FoundationStereo
공식 코드FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
https://github.com/NVlabs/GroupViT
공식 코드GroupViT: Semantic Segmentation Emerges from Text Supervision
https://github.com/NVlabs/wetectron
공식 코드Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection
https://github.com/Na-Z/sess
공식 코드https://github.com/NathanUA/BASNet
공식 코드Boundary-Aware Segmentation Network for Mobile and Web Applications
https://github.com/NathanUA/U-2-Net
공식 코드U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
https://github.com/NatiOfir/SuperPixelFusion
공식 코드Multispectral image fusion based on super pixel segmentation
https://github.com/Natlem/M-HTCN
공식 코드Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer
https://github.com/NesterukSergey/segmentation_image_augmentation
공식 코드Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case
https://github.com/NeurAI-Lab/UniNet
공식 코드https://github.com/NeuralSec/Daedalus-attack
공식 코드Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
https://github.com/NeuromorphicComputing/S-TEC
공식 코드https://github.com/Nicholasli1995/EgoNet
공식 코드Exploring intermediate representation for monocular vehicle pose estimation
https://github.com/Nightmare-n/UniPAD
공식 코드UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
https://github.com/OIPLab-DUT/CoNet
공식 코드Accurate RGB-D Salient Object Detection via Collaborative Learning
https://github.com/OPIXray-author/OPIXray
공식 코드https://github.com/OUCVisionGroup/TISC-Net
공식 코드https://github.com/OVAD-Benchmark/ovad-bechmark-code
공식 코드https://github.com/OfficialBishal/GeomMaskAttack
공식 코드https://github.com/Oleffa/LeagueAI
공식 코드https://github.com/OliverRensu/GLSTR
공식 코드Unifying Global-Local Representations in Salient Object Detection with Transformer
https://github.com/OpenDriveLab/DetAny3D
공식 코드https://github.com/OpenGVLab/M3I-Pretraining
공식 코드Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information
https://github.com/OpenGVLab/PonderV2
공식 코드PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
https://github.com/OpenRobotLab/Grounded_3D-LLM
공식 코드https://github.com/OpenSpaceAI/Nesie
공식 코드Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detection
https://github.com/Owen-Liuyuxuan/visualDet3D
공식 코드Ground-aware Monocular 3D Object Detection for Autonomous Driving
https://github.com/Ozzyz/carla-data-export
공식 코드https://github.com/PJLallen/InstanceSAM2Eval
공식 코드Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation
https://github.com/PKUbahuangliuhe/CBNet
공식 코드CBNet: A Novel Composite Backbone Network Architecture for Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.