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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/KieDani/SegformerPlusPlus

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

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/PaddlePaddle/PaddleSeg

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/TomG008/SkyNet

FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge

https://github.com/UAws/CV-3315-Is-All-You-Need

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/VikParuchuri/surya

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/adam-vandervorst/pybhv

HDCC: A Hyperdimensional Computing compiler for classification on embedded systems and high-performance computing

https://github.com/alessiomora/unlearning_fl

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/assert-kth/vrepair

Neural Transfer Learning for Repairing Security Vulnerabilities in C Code

https://github.com/awsm-research/vulrepair

Neural Transfer Learning for Repairing Security Vulnerabilities in C Code

https://github.com/bdqnghi/bi-tbcnn

Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks

https://github.com/chenghan111/dnc

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/code-implementation1/Code8/tree/main/Segformer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/dzhaoxd/seco

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/flshyi/PLSLAM

PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments

https://github.com/google-research/self-organising-systems/blob/master/notebooks/%CE%BCNCA_jax.ipynb

$μ$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata

https://github.com/google-research/self-organising-systems/blob/master/notebooks/%CE%BCNCA_pytorch.ipynb

$μ$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata

https://github.com/huangqx/image_shape_align

OpenGM: A C++ Library for Discrete Graphical Models

https://github.com/huggingface/transformers

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/iml130/nncg

A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained Systems

https://github.com/jiangwx/SkyNet-ZCU104

FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge

https://github.com/kikacaty/RAP_Benchmark

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/lucidrains/segformer-pytorch

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/macdonaldezra/minesegsat

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/maxpark/SkyNet-1

FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge

https://github.com/mew-cx/chaitin_lisp

The Limits of Mathematics (in C)

https://github.com/min-zou/opengm

OpenGM: A C++ Library for Discrete Graphical Models

https://github.com/opengm/opengm

OpenGM: A C++ Library for Discrete Graphical Models

https://github.com/pangfumin/pl-slam

PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments

https://github.com/pwc-1/Paper-10/tree/main/segformer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/sithu31296/semantic-segmentation

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/tihonovcore/model

Structural Language Models of Code

https://github.com/toqitahamid/gasformer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/zlh0527/image-shape-align

OpenGM: A C++ Library for Discrete Graphical Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.