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

https://github.com/dksakkos/illumination_augmentation

Illumination-Based Data Augmentation for Robust Background Subtraction

https://github.com/dlpbc/keras-kinetics-i3d

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

https://github.com/dmeoli/neuro-sat

Graph Attention Networks

https://github.com/dmis-lab/temporalhead

Knowledge Circuits in Pretrained Transformers

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/sgc

Simplifying Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/arma

Graph Neural Networks with convolutional ARMA filters

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/sgc

Simplifying Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/sgc

Simplifying Graph Convolutional Networks

https://github.com/dmonterom/face_recognition_TF2

Group Normalization

https://github.com/dnozza/profanity-obfuscation

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

https://github.com/doantienthongbku/ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/doc-doc/NExT-OE

NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions

https://github.com/dokyeongK/Pytorch_SRCNN

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/dokyeongK/Single-Image-Super-Resolution-Reimplemenation

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/dongkwonjin/Semantic-Line-DRM

Interactive Video Object Segmentation Using Global and Local Transfer Modules

https://github.com/dongkwonjin/Semantic-Line-SLNet

Interactive Video Object Segmentation Using Global and Local Transfer Modules

https://github.com/donshen/pointnet.phasedetection

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

https://github.com/doronharitan/human_activity_recognition_LRCN

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

https://github.com/dorsadadjoo/Distance_Estimation

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/dpfried/action-segmentation

Cross-task weakly supervised learning from instructional videos

https://github.com/dreamh1gh/awesome-srl

Visual Semantic Role Labeling

https://github.com/drhdr-user/drhdr

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/drv-agwl/ViViT-pytorch

ViViT: A Video Vision Transformer

https://github.com/ds4dm/sGat

Graph Attention Networks

https://github.com/ds4dm/sparse-gcn

Graph Attention Networks

https://github.com/dsl-lab/fvmd-frechet-video-motion-distance

Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos

https://github.com/dsshim0125/grmc

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/duyhlzu/GMG

Unsupervised Learning of Video Representations using LSTMs

https://github.com/dvl-tum/mot_neural_solver

Tracking without bells and whistles

https://github.com/dvl-tum/motsynth-baselines

Tracking without bells and whistles

https://github.com/dwtstore/sfm1

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.