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

https://github.com/romaintha/pytorch_pointnet

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

https://github.com/rort1989/HDM

https://github.com/roshankoirala/Fire_Detection_Model

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/roshantac/DenseDepth

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/roudimit/AVLnet

HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips

https://github.com/rozumden/ShapeFromBlur

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

https://github.com/rozumden/tbd

Intra-frame Object Tracking by Deblatting

https://github.com/rozumden/tbd3d

Intra-frame Object Tracking by Deblatting

https://github.com/rramjee/DenseDepth

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/rrxi/CrevNet

https://github.com/rsha0812/DenseDepth

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/rubenvillegas/iclr2017mcnet

Decomposing Motion and Content for Natural Video Sequence Prediction

https://github.com/rubenvillegas/icml2017hierchvid

Learning to Generate Long-term Future via Hierarchical Prediction

https://github.com/ruc-aimc-lab/teachclip

https://github.com/rucmlcv/L2M-GAN

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

https://github.com/rudolfwilliam/satellite_image_forecasting

https://github.com/ruinmessi/RFBNet

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/rutajoshi/cs231n-project

Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?

https://github.com/ryanchesler/comma-speed-challenge

UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

https://github.com/ryanhalabi/starcraft_super_resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/ryanhoque/fabric-vsf

VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation

https://github.com/ryanxingql/image-quality-assessment-toolbox

Learning a No-Reference Quality Metric for Single-Image Super-Resolution

https://github.com/s-gupta/v-coco

Visual Semantic Role Labeling

https://github.com/saaries/PointNet

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

https://github.com/sadari1/TumorDetectionDeepLearning

Temporal Convolutional Networks for Action Segmentation and Detection

https://github.com/safwankdb/ReCoNet-PyTorch

ReCoNet: Real-time Coherent Video Style Transfer Network

https://github.com/sagnik1511/Transfer-Learning-with-Python

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sahilunagar/VGG16-model

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/saikasyap/Video-Embeddings

Unsupervised Learning of Video Representations using LSTMs

https://github.com/saimj7/Action-Recognition-in-Real-Time

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sainzerjj/sferd

Consistency Models

https://github.com/saiyan-world/goku

Goku: Flow Based Video Generative Foundation Models

https://github.com/sakshamgupta006/video-to-video-synthesis

Video-to-Video Synthesis

https://github.com/sakurusurya2000/CNN

Semantic Perceptual Image Compression using Deep Convolution Networks

https://github.com/salesforce/ensemble-of-averages

Billion-scale semi-supervised learning for image classification

https://github.com/salinasJJ/BBaction

Video Classification with Channel-Separated Convolutional Networks

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