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

https://github.com/Paulpauladq/HighSpeedFaceTracker

High-Speed Tracking with Kernelized Correlation Filters

https://github.com/Pea-Shooter/ADNet

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/Pecneb/computer_vision_research

Simple Online and Realtime Tracking with a Deep Association Metric

https://github.com/PeterWang512/FALdetector

Detecting Photoshopped Faces by Scripting Photoshop

https://github.com/Phimos/Paddle-PointNet

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

https://github.com/PhysGame/PhysGame

PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos

https://github.com/PolyArch/fp-diannao

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Powercoder64/TAA-GCN

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/Pradyumn-Patil/updated-research-paper-links-

Video Content Classification using Deep Learning

https://github.com/Pratik08/vis-dss

Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization

https://github.com/Pulkitdzrt/ML-Image-Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/PumpkinYing/GAT

Graph Attention Networks

https://github.com/Puzer/stylegan-encoder

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/QUVA-Lab/timeception

Timeception for Complex Action Recognition

https://github.com/Qdriving/Swin-TransformerV2-in-PP

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/RAGHAV2998/Everybody-Can-Dance-Now-Video-game-version-

Everybody Dance Now

https://github.com/Reatris/Visualizing-and-Understanding-Convolutional-Networks

Visualizing and Understanding Convolutional Networks

https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection

Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing

https://github.com/ReshmikaD/Artistic-Style-Transfer-CNN

Visualizing and Understanding Convolutional Networks

https://github.com/RezisEwig/esrgan

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/RoboTurk-Platform/roboturk_real_dataset

Stochastic Variational Video Prediction

https://github.com/Robotmurlock/Motrack

Simple Online and Realtime Tracking with a Deep Association Metric

https://github.com/Roc-Ng/DeepMIL

Real-world Anomaly Detection in Surveillance Videos

https://github.com/RonyAbecidan/Mantranet-pytorch

https://github.com/RoshanRane/PredNet-and-Predictive-Coding-A-Critical-Review

PredNet and Predictive Coding: A Critical Review

https://github.com/RoshanRane/segmentation-moving-MNIST

Unsupervised Learning of Video Representations using LSTMs

https://github.com/Ruby1302/DenseDepth

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/Ruby1302/DenseDepth_1

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/RudreshVeerkhare/StyleGan

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Run542968/ProCL

Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning

https://github.com/RunzhaoHuang/DeepSort_YOLOV5_OnScreen

Simple Online and Realtime Tracking with a Deep Association Metric

https://github.com/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Ruturaj123/VGG16-Keras

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Ryosaeba8/Anomaly_detection

Deep Anomaly Detection with Deviation Networks

https://github.com/S-aiueo32/lpips-pytorch

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

https://github.com/SBPL-Cruz/perch_pose_sampler

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

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