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

https://github.com/ftorres11/saliencysense

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

https://github.com/fxia22/pointnet.pytorch

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

https://github.com/gabeur/mmt

Multi-modal Transformer for Video Retrieval

https://github.com/gaetano-signorelli/Attention-Style-Transfer

AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer

https://github.com/gajdosech2/vehicle-speed-estimation-yolov6-3d

Detection of 3D Bounding Boxes of Vehicles Using Perspective Transformation for Accurate Speed Measurement

https://github.com/galkampel/HyperNetworks

Graph Attention Networks

https://github.com/gan3sh500/octaveconv-pytorch

Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

https://github.com/gangasani-anusha/Short-Story_Assignment

Visual Question Answering: which investigated applications?

https://github.com/gaoqianmiao/Summer-project

Action Recognition using Visual Attention

https://github.com/garfield-kh/posetriplet

Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition

https://github.com/garroud/human-motion-prediction-pytorch

On human motion prediction using recurrent neural networks

https://github.com/garythung/torch-lrcn

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

https://github.com/garyzhao/FRGAN

Learning to Forecast and Refine Residual Motion for Image-to-Video Generation

https://github.com/gauraviiita/Visualization-of-Human3.6M-Dataset

On human motion prediction using recurrent neural networks

https://github.com/gayanku/FDGATII

Graph Attention Networks

https://github.com/gcucurull/jax-gat

Graph Attention Networks

https://github.com/gd-goblin/C-3PO_Motion_Retargeting_Module

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

https://github.com/gd-goblin/C-3PO_RobotControlModule

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

https://github.com/george-gca/sr-pytorch-lightning

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/georgeyiasemis/2D-Convolutional-Recurrent-Neural-Networks-with-PyTorch

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

https://github.com/gerasmark/Reproducing-Unseen-Modality-Interaction

Learning Unseen Modality Interaction

https://github.com/gfxdisp/FovVideoVDP

https://github.com/ginkyenglee/Explaining_Decision_of_Time_Series_Data

Explaining NonLinear Classification Decisions with Deep Taylor Decomposition

https://github.com/ginn24/RefineDet-1

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/github-zbx/ST-GCN

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/gitlimlab/Representation-Learning-by-Learning-to-Count

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/gitzh-chen/rmlr

Riemannian Multinomial Logistics Regression for SPD Neural Networks

https://github.com/giuseppefutia/link-prediction-code

Graph Attention Networks

https://github.com/gmum/mevgan

MeVGAN: GAN-based Plugin Model for Video Generation with Applications in Colonoscopy

https://github.com/gmum/umwpl2021

Graph Attention Networks

https://github.com/goddoe/hide-and-seek

Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization

https://github.com/goluck-konuko/animation-based-codecs

Ultra-low bitrate video conferencing using deep image animation

https://github.com/gongzix/neuroclips

Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

https://github.com/google-deepmind/kinetics-i3d

The Kinetics Human Action Video Dataset

https://github.com/google-deepmind/proactive_t2i_agents

Evaluating Text-to-Visual Generation with Image-to-Text Generation

https://github.com/google-research-datasets/eev

EEV: A Large-Scale Dataset for Studying Evoked Expressions from Video

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