https://github.com/MindSpore-paper-code-3/code5/tree/main/REDNet30
RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation
Papers with Code 아카이브
NLP based Person Retrival
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-paper-code-3/code5/tree/main/Segformer
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/MindSpore-paper-code-3/code5/tree/main/SemanticHumanMatting
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
https://github.com/MindSpore-paper-code-3/code5/tree/main/relationnet
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder
https://github.com/MindSpore-paper-code-3/code6/tree/main/ADNet
https://github.com/MindSpore-paper-code-3/code6/tree/main/AttGAN
AttGAN: Facial Attribute Editing by Only Changing What You Want
https://github.com/MindSpore-paper-code-3/code6/tree/main/advanced_east
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
https://github.com/MindSpore-paper-code-3/code7/tree/main/FCOS
NAS-FCOS: Fast Neural Architecture Search for Object Detection
https://github.com/MindSpore-paper-code-3/code7/tree/main/fastscnn
https://github.com/MindSpore-paper-code-3/code8/tree/main/dcrnn
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
https://github.com/MindSpore-paper-code-3/code8/tree/main/detr
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
https://github.com/MindSpore-paper-code-3/code8/tree/main/dnet_nas
D-Net: Siamese based Network with Mutual Attention for Volume Alignment
https://github.com/MindSpore-paper-code-3/code8/tree/main/fastscnn
https://github.com/MindSpore-paper-code-3/code9/tree/main/OCRNet
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
https://github.com/MindSpore-paper-code-3/code9/tree/main/gnmt_v2
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/MindSpore-paper-code-3/code9/tree/main/mass
MASS: Masked Sequence to Sequence Pre-training for Language Generation
https://github.com/MindSpore-paper-code-3/code9/tree/main/speech_transformer
A Simplified Fully Quantized Transformer for End-to-end Speech Recognition
https://github.com/MindSpore-paper-code-3/code9/tree/main/textcnn
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/MindSpore-scientific-2/code-14/tree/main/longformer
Unlimiformer: Long-Range Transformers with Unlimited Length Input
https://github.com/MindSpore-scientific-2/code-3/tree/main/Tom
TOM-Net: Learning Transparent Object Matting from a Single Image
https://github.com/MindSpore-scientific-2/code-4/tree/main/D-Unet_Mindspore
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
https://github.com/MindSpore-scientific-2/code-5/tree/main/D-Unet_Mindspore
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
https://github.com/MindSpore-scientific-2/code-5/tree/main/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/MindSpore-scientific-2/code-8/tree/main/pytorch_convgru
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/MindSpore-scientific-2/code-9/tree/main/D-Unet_Mindspore
https://github.com/MindSpore-scientific-2/code-9/tree/main/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/MindSpore-scientific/code-1/tree/main/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/MindSpore-scientific/code-10/tree/main/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/MindSpore-scientific/code-11/tree/main/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
https://github.com/MindSpore-scientific/code-11/tree/main/Least_Generative
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
https://github.com/MindSpore-scientific/code-11/tree/main/Translation-Invariant/model
https://github.com/MindSpore-scientific/code-11/tree/main/mgan-mindspore
MGANet: A Robust Model for Quality Enhancement of Compressed Video
https://github.com/MindSpore-scientific/code-12/tree/main/CS-F-LTR
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
https://github.com/MindSpore-scientific/code-12/tree/main/D-Unet_Mindspore
https://github.com/MindSpore-scientific/code-12/tree/main/FDDE/train-fine
https://github.com/MindSpore-scientific/code-13/tree/main/Knowing-When-to-Look-Adaptive-Attention
Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.