Papers with Code 아카이브
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/Howal/DNL-Object-Detection
공식 코드https://github.com/HsinYingLee/OPN
공식 코드https://github.com/HuangJunJie2017/BEVDet
공식 코드BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View
https://github.com/Hwang64/MLKP
공식 코드Multi-scale Location-aware Kernel Representation for Object Detection
https://github.com/Hwang64/PSIS
공식 코드Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
https://github.com/Hydragon516/SPSN
공식 코드SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
https://github.com/I2-Multimedia-Lab/A2S-v3
공식 코드Unified Unsupervised Salient Object Detection via Knowledge Transfer
https://github.com/I2-Multimedia-Lab/M3Net
공식 코드M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection
https://github.com/I2-Multimedia-Lab/UGRAN
공식 코드Uncertainty Guided Refinement for Fine-Grained Salient Object Detection
https://github.com/I2RDL2/ASTAR-3D
공식 코드A*3D Dataset: Towards Autonomous Driving in Challenging Environments
https://github.com/IBM/RegionViT
공식 코드RegionViT: Regional-to-Local Attention for Vision Transformers
https://github.com/IDEA-Research/detrex
공식 코드DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
https://github.com/IDEACVR/DINO
공식 코드DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
https://github.com/IMPLabUniPr/mmdetection
공식 코드Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing
https://github.com/INFINIQ-AI1/RISF
공식 코드Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection
https://github.com/IcebergKnight/NMS-Loss
공식 코드NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection
https://github.com/IlyaOvodov/AngelinaDataset
공식 코드https://github.com/InfLoop111/DCA
공식 코드DCA: Dividing and Conquering Amnesia in Incremental Object Detection
https://github.com/Infernolia/WEDGE
공식 코드WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models
https://github.com/InnovArul/vidreid_cosegmentation
공식 코드Co-segmentation Inspired Attention Module for Video-based Computer Vision Tasks
https://github.com/Instinct323/wiou
공식 코드Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism
https://github.com/IntelLabs/pytorchalfi
공식 코드https://github.com/IrohXu/VCog-Bench
공식 코드What is the Visual Cognition Gap between Humans and Multimodal LLMs?
https://github.com/IuliiaSaveleva/ALFA
공식 코드ALFA: Agglomerative Late Fusion Algorithm for Object Detection
https://github.com/JCZ404/Semi-DETR
공식 코드Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
https://github.com/JDAI-CV/LIO
공식 코드Look-into-Object: Self-supervised Structure Modeling for Object Recognition
https://github.com/JHKim-snu/GVCCI
공식 코드GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic Manipulation
https://github.com/JacobWashburn-USDA/MatchPlant
공식 코드MatchPlant: An Open-Source Pipeline for UAV-Based Single-Plant Detection and Data Extraction
https://github.com/Jacoo-ai/HIC-Yolov5
공식 코드https://github.com/Jakaria08/Filter_Enhance_Detect
공식 코드https://github.com/JakubSochor/BoxCars
공식 코드https://github.com/James-Yuichi-Sato/MLSYS-2022-Video-Pipeline
공식 코드https://github.com/Jamie725/RGBT-detection
공식 코드https://github.com/Jasonxu1225/Improved-Lightweight-YOLOv5-for-Face-Mask-Detection
공식 코드An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection
https://github.com/JeffCHEN2017/WSSTG
공식 코드Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.