https://github.com/sanantoniochili/PointCloud_KNN
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sandeeplbscek/awesome-deeplearning-resources
https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/sandman002/One-Style-is-All-You-Need-to-Generate-a-Video
https://github.com/sanixa/CADA-VAE-pytorch
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
https://github.com/santhoshpkumar/Hand-gesture-recognition-using-neural-networks
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/sarthakTUM/roofn3d
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/sayakpaul/Revisiting-Pooling-in-CNNs
https://github.com/sberbank-ai/ru-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/sbhonde1/Multimodal-Learning
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal Training
https://github.com/scouTT1/C3D
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/sdimi/cardiofitness
Learning Generalizable Physiological Representations from Large-scale Wearable Data
https://github.com/seanmcgovern21/Machine-Learning-CS539
Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks
https://github.com/sebastiantiesmeyer/deeplabchop3d
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/secretlyvogon/IndRNNTF
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/secretlyvogon/Neural-Network-Implementations
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/securade/hub
Action Recognition based Industrial Safety Violation Detection
https://github.com/selmiss/gp-tlstgcn
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/semskurto/APTOS
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/sendeniz/creative-adverserial-net
https://github.com/sense-x/uniformer
https://github.com/seominseok0429/Real-world-Anomaly-Detection-in-Surveillance-Videos-pytorch
https://github.com/seominseok0429/inception-I3D-NON-LOCAL
https://github.com/ser-art/RAE-vs-AE
https://github.com/setharram/facenet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/seunghwan1228/CNN_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/shao-chi/ImageCaption
https://github.com/shashidhar100/Comparison-of-gradcam-and-gradcam_plus_plus_keras
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/shenweichen/GraphNeuralNetwork
https://github.com/shi27feng/transformers.satisfy
https://github.com/shijianjian/efficientnet-pytorch-3d
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/shivammehta25/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/shkarupa-alex/tfclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/shlizee/Predict-Cluster
PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition
https://github.com/shobrook/sequitur
Beyond Short Snippets: Deep Networks for Video Classification
https://github.com/shubhambitsg/activity-recognition
Moments in Time Dataset: one million videos for event understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.