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

https://github.com/mehdidc/DALLE_clip_score

GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions

https://github.com/mehryar72/RS-SUM

https://github.com/meliketoy/graph-cnn.pytorch

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/memory-attention-networks/MANs

Memory Attention Networks for Skeleton-based Action Recognition

https://github.com/mengab/Versatile-Video-Coding

Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure

https://github.com/mengxingshifen1218/pointnet.pytorch

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/merazlab/3D_Deep_Learning_Link

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/metalbubble/TRN-pytorch

Temporal Relational Reasoning in Videos

https://github.com/metalbubble/moments_models

Moments in Time Dataset: one million videos for event understanding

https://github.com/metra4ok/MLSTM-FCN-Pytorch

Multivariate LSTM-FCNs for Time Series Classification

https://github.com/metrofun/E3D-LSTM

https://github.com/mezo11/2

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

https://github.com/mfaisal59/EpipolarScore

EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency

https://github.com/mfaisal59/RBSF

EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency

https://github.com/mfayk/cu_icar_image_captioning

Deep Learning for Video Classification and Captioning

https://github.com/mgonzalezrivero/reef_learning

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/michael13162/DoodleGAN

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/microndla/mobilevitv3

CVNets: High Performance Library for Computer Vision

https://github.com/microsoft/2D-TAN

Learning Sparse 2D Temporal Adjacent Networks for Temporal Action Localization

https://github.com/microsoft/FairMOT

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/microsoft/MLCVQA

Full Reference Video Quality Assessment for Machine Learning-Based Video Codecs

https://github.com/microsoft/MLOps_VideoAnomalyDetection

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

https://github.com/microsoft/UDA

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/microsoft/VLM-Video-Action-Localization

Open-Vocabulary Action Localization with Iterative Visual Prompting

https://github.com/microsoft/activemlp

Neural Video Compression with Diverse Contexts

https://github.com/microsoft/computervision-recipes

A Closer Look at Spatiotemporal Convolutions for Action Recognition

https://github.com/microsoft/distance-learner

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/microsoft/unicl

Florence: A New Foundation Model for Computer Vision

https://github.com/mihirp1/Deep_Learning_Project_CPSC8810

Visualizing and Understanding Convolutional Networks

https://github.com/mikekestemont/deepscript

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mikel-brostrom/Yolov3_DeepSort_Pytorch

Simple Online and Realtime Tracking with a Deep Association Metric

https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch

Simple Online and Realtime Tracking with a Deep Association Metric

https://github.com/mikel-brostrom/boxmot

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

https://github.com/mikel-brostrom/yolov7

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/mil-tokyo/FTGAN

Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture

https://github.com/milenagazdieva/extremalneuraloptimaltransport

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

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