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2D Object Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/cuong-pv/FAM-KD
공식 코드https://github.com/cuppp1998/360_object_tracking
공식 코드Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis
https://github.com/custommetrics-sst/sst_customevaluationmetrics
공식 코드SST-EM: Advanced Metrics for Evaluating Semantic, Spatial and Temporal Aspects in Video Editing
https://github.com/cv-core/MIT-Driverless-CV-TrainingInfra
공식 코드https://github.com/cv-zhangxin/akconv
공식 코드LDConv: Linear deformable convolution for improving convolutional neural networks
https://github.com/cv-zhangxin/ldconv
공식 코드LDConv: Linear deformable convolution for improving convolutional neural networks
https://github.com/cv516buaa/ov-vg
공식 코드https://github.com/cviaai/dasha
공식 코드DASHA: Decentralized Autofocusing System with Hierarchical Agents
https://github.com/cvignac/top-n
공식 코드Top-N: Equivariant set and graph generation without exchangeability
https://github.com/cvlab-epfl/mvflow
공식 코드Multi-view Tracking Using Weakly Supervised Human Motion Prediction
https://github.com/cvlab-stonybrook/Weighting-Pseudo-Labels
공식 코드https://github.com/cvlab-yonsei/HVPR
공식 코드HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection
https://github.com/cvmi-lab/codet
공식 코드CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
https://github.com/cvmi-lab/fs3d
공식 코드Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
https://github.com/cvmi-lab/sparsekd
공식 코드Towards Efficient 3D Object Detection with Knowledge Distillation
https://github.com/cvmi-lab/syncood
공식 코드https://github.com/cxliu0/oa-mil
공식 코드https://github.com/cxt98/ACF_perception
공식 코드Manipulation-Oriented Object Perception in Clutter through Affordance Coordinate Frames
https://github.com/cxy1997/3D_adapt_auto_driving
공식 코드Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
https://github.com/cxy1997/DiffuBox
공식 코드https://github.com/cybercore-co-ltd/CoLAD
공식 코드https://github.com/cybercore-co-ltd/Single-Stage-Common-Object-Detection
공식 코드Single Stage Class Agnostic Common Object Detection: A Simple Baseline
https://github.com/cybercore-co-ltd/colad_paper
공식 코드https://github.com/cybonic/misagriculture
공식 코드Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches
https://github.com/cymichael/roi_detection
공식 코드Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images -- Nevus & Melanoma
https://github.com/cyprienquemeneur/fedpylot
공식 코드FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles
https://github.com/czczup/vit-adapter
공식 코드https://github.com/czhang0528/MosaicOS
공식 코드MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection
https://github.com/czm369/mixpl
공식 코드https://github.com/d-li14/HBONet
공식 코드https://github.com/d-robotics-ai-lab/dosod
공식 코드https://github.com/dadung/dge-cda
공식 코드Assessing Domain Gap for Continual Domain Adaptation in Object Detection
https://github.com/daeunfullgrace/dyra
공식 코드DyRA: Portable Dynamic Resolution Adjustment Network for Existing Detectors
https://github.com/daijifeng001/r-fcn
공식 코드R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/dair-iitd/degpr
공식 코드DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.