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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ming71/DAL
공식 코드Dynamic Anchor Learning for Arbitrary-Oriented Object Detection
https://github.com/ming71/RIDet
공식 코드Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss
https://github.com/mingbohong/yola
공식 코드You Only Look Around: Learning Illumination Invariant Feature for Low-light Object Detection
https://github.com/minghanz/trafcam_3d
공식 코드Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography
https://github.com/mingjiayang/sgsdi
공식 코드Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images
https://github.com/mingyao1120/tr-detr
공식 코드TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection
https://github.com/minkiml/NFM
공식 코드Neural Fourier Modelling: A Highly Compact Approach to Time-Series Analysis
https://github.com/miquel-espinosa/no-time-to-train
공식 코드No time to train! Training-Free Reference-Based Instance Segmentation
https://github.com/miracle-center/ctspine1k
공식 코드CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography
https://github.com/mirthai/fast-ddpm
공식 코드Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
https://github.com/mirthai/gdm-ve
공식 코드Geodesic Diffusion Models for Medical Image-to-Image Generation
https://github.com/mit-han-lab/bevfusion
공식 코드BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
https://github.com/mit-han-lab/efficientvit
공식 코드EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/mit-han-lab/pvcnn
공식 코드https://github.com/mit-han-lab/spvnas
공식 코드Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/mjq11302010044/TPGSR
공식 코드https://github.com/mjq11302010044/real-ce
공식 코드A Benchmark for Chinese-English Scene Text Image Super-resolution
https://github.com/mjq11302010044/tatt
공식 코드A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution
https://github.com/mjseong0414/CRT-Fusion
공식 코드CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection
https://github.com/mjseong0414/RCM-Fusion
공식 코드RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection
https://github.com/mk-minchul/attend-and-compare
공식 코드https://github.com/mkang315/CST-YOLO
공식 코드CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer
https://github.com/mkang315/bgf-yolo
공식 코드BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
https://github.com/mkang315/pk-yolo
공식 코드PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slices
https://github.com/mkang315/rcs-yolo
공식 코드RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection
https://github.com/mkchoi-0323/modified_refinedet
공식 코드Development of Fast Refinement Detectors on AI Edge Platforms
https://github.com/ml-lab-htw/iterative-annotate
공식 코드Feedback-driven object detection and iterative model improvement
https://github.com/mlcaepiee/simuav
공식 코드https://github.com/mlpc-ucsd/CoaT
공식 코드https://github.com/mlvlab/RALF
공식 코드https://github.com/mlvlab/cpchoi
공식 코드https://github.com/mlvlab/davi
공식 코드Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
https://github.com/mlvlab/drone_ai_challenge
공식 코드https://github.com/mlvlab/drone_task1
공식 코드https://github.com/mlvlab/drone_task4
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.