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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/microsoft/fiber
공식 코드Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone
https://github.com/microsoft/hnms
공식 코드Hashing-based Non-Maximum Suppression for Crowded Object Detection
https://github.com/microsoft/regionclip
공식 코드https://github.com/microsoft/scene_graph_benchmark
공식 코드https://github.com/microsoft/swin3d
공식 코드Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
https://github.com/microsoft/table-transformer
공식 코드PubTables-1M: Towards comprehensive table extraction from unstructured documents
https://github.com/microsoft/tutel
공식 코드https://github.com/microsoft/unilm/tree/master/beit
공식 코드Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
https://github.com/microsoft/vision-longformer
공식 코드Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding
https://github.com/miguelag99/efficient-instance-prediction
공식 코드Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction
https://github.com/mihirp1998/Disentangling-3D-Prototypical-Nets
공식 코드Disentangling 3D Prototypical Networks For Few-Shot Concept Learning
https://github.com/mikasa3lili/ssd-monodetr
공식 코드SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection
https://github.com/mikeadamer/mag-metric
공식 코드https://github.com/mikuhatsune/wsod_transfer
공식 코드Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer
https://github.com/mileyan/Pseudo_Lidar_V2
공식 코드Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
https://github.com/mileyan/pseudo-LiDAR_e2e
공식 코드https://github.com/mileyan/pseudo_lidar
공식 코드https://github.com/mililab/roma
공식 코드RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
https://github.com/milinpatel07/SOTIF-PCOD
공식 코드https://github.com/miltonbd/ECCV_2018_pedestrian_detection_challenege
공식 코드Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge
https://github.com/mindgarage-shan/trans_object_detection_survey
공식 코드https://github.com/mindgarage-shan/transformer_object_detection_survey
공식 코드https://github.com/mindspore-ai/models/tree/master/research/cv/ISyNet
공식 코드ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/mindspore-ai/models/tree/master/research/cv/TokenFusion
공식 코드https://github.com/mindspore-lab/models
공식 코드https://github.com/mindspore-lab/models/tree/master/research/huawei-noah/DECO
공식 코드https://github.com/ming71/CFC-Net
공식 코드https://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/miquel-espinosa/no-time-to-train
공식 코드No time to train! Training-Free Reference-Based Instance Segmentation
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.