Papers with Code 아카이브
LIDAR Semantic Segmentation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/L-Reichardt/Cylinder3D-updated-CUDA
Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation
https://github.com/LONG-9621/SparseConvNet
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
https://github.com/LanqingLi/Chromosome
https://github.com/Lemoners/Attention-Is-All-You-Need
https://github.com/LeoWood/bert-horovod
https://github.com/LiamMaclean216/Pytorch-Chatbot
https://github.com/LiamMaclean216/Pytorch-Transfomer
https://github.com/LoveYang/bert_test
https://github.com/Lsdefine/attention-is-all-you-need-keras
https://github.com/MCLYang/RhythmGAN_pytorch
https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted
https://github.com/MTCloudVision/mxnet-dssd
https://github.com/MaZhiyuanBUAA/bert-tf1.4.0
https://github.com/Maple728/transformer
https://github.com/MarcBS/keras
https://github.com/MashaKubyshina/learning_ML
https://github.com/Matthewdowney18/Transformer_Dialogue
https://github.com/Maz101/Bert
https://github.com/Maz101/pretrainingdataset
https://github.com/MenGuangwen-CN-0411/Simple-Use-of-SSD
https://github.com/MichaelZhouwang/LMlexsub
https://github.com/Mind23-2/MindCode-21
LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention
https://github.com/Mind23-2/MindCode-23
LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention
https://github.com/Mind23-2/MindCode-82
https://github.com/MindCode-4/code-5/tree/main/transformer
https://github.com/MindSpore-paper-code-3/code9/tree/main/SSD
https://github.com/MindSpore-scientific/code-10/tree/main/rand_conv-master
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
https://github.com/MindSpore-scientific/code-11/tree/main/Translation-Invariant/model
https://github.com/MindSpore-scientific/code-8/tree/main/Translation-Invariant/model
https://github.com/MindSpore-scientific/code-8/tree/main/rand_conv-master
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
https://github.com/MirunaPislar/multi-head-attention-labeller
https://github.com/Misoknisky/Bert-MultiGpu
https://github.com/MohamedAbdelsalam9/TT-Transformer
https://github.com/MonsterPeng/caffe_ssd
https://github.com/MrAI-Rohan/SSDImplementation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.