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/gamepai0811/fmsa
공식 코드https://github.com/ganwanshui/es6d
공식 코드ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework
https://github.com/ganwanshui/simpleoccupancy
공식 코드A Simple Framework for 3D Occupancy Estimation in Autonomous Driving
https://github.com/gaopengcuhk/Container
공식 코드https://github.com/gaopengcuhk/SMCA-DETR
공식 코드End-to-End Object Detection with Adaptive Clustering Transformer
https://github.com/gaoyingjay/ps-ttl
공식 코드PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detection
https://github.com/garfield-cpp/hgsfusion
공식 코드HGSFusion: Radar-Camera Fusion with Hybrid Generation and Synchronization for 3D Object Detection
https://github.com/garrickbrazil/M3D-RPN
공식 코드M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
https://github.com/garyson1204/hginet
공식 코드https://github.com/gatedip/gdip-yolo
공식 코드GDIP: Gated Differentiable Image Processing for Object-Detection in Adverse Conditions
https://github.com/gatorsense/matl
공식 코드Multi-Task Learning with Multi-Annotation Triplet Loss for Improved Object Detection
https://github.com/gauravreddy08/OcularAI
공식 코드MagicEye: An Intelligent Wearable Towards Independent Living of Visually Impaired
https://github.com/gautamtata/DeepPlastic
공식 코드A Robotic Approach towards Quantifying Epipelagic Bound Plastic Using Deep Visual Models
https://github.com/geoffreychen777/vc
공식 코드Semi-supervised Object Detection via Virtual Category Learning
https://github.com/geonhobang/RadarDistill
공식 코드https://github.com/georgeretsi/segfreekws
공식 코드https://github.com/georghess/pmb-nll
공식 코드https://github.com/georghess/voxel-mae
공식 코드Masked Autoencoder for Self-Supervised Pre-training on Lidar Point Clouds
https://github.com/georgia-tech-synergy-lab/clamp-vit
공식 코드CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs
https://github.com/geshang777/FOCUS
공식 코드https://github.com/geshang777/Seg-R1
공식 코드Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
https://github.com/gewelsji/dgnet
공식 코드Deep Gradient Learning for Efficient Camouflaged Object Detection
https://github.com/gewelsji/errnet
공식 코드Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network
https://github.com/gewu-lab/csol_tpami2021
공식 코드Class-aware Sounding Objects Localization via Audiovisual Correspondence
https://github.com/ggjy/DeFeat.pytorch
공식 코드https://github.com/ggjy/Hire-Wave-MLP.pytorch
공식 코드https://github.com/ggjy/HitDet.pytorch
공식 코드Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
https://github.com/ghostish/objectcentricocccompletion
공식 코드Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection
https://github.com/giakoumoglou/synco
공식 코드https://github.com/giannifranchi/NAO_morpho
공식 코드Learning Deep Morphological Networks with Neural Architecture Search
https://github.com/giaracvi/NEFCI
공식 코드https://github.com/gicait/plitter
공식 코드pLitterStreet: Street Level Plastic Litter Detection and Mapping
https://github.com/gidariss/AttractioNet
공식 코드Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
https://github.com/gidariss/locnet
공식 코드LocNet: Improving Localization Accuracy for Object Detection
https://github.com/gidariss/mrcnn-object-detection
공식 코드Object detection via a multi-region & semantic segmentation-aware CNN model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.