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/Anou9531/GAT
https://github.com/Aprilkaka/nonlocal_mindspore
https://github.com/AshishSingh2261/GAN
https://github.com/Atze00/MoViNet-pytorch
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/Aveek-Saha/Graph-Attention-Net
https://github.com/B-HAR-HumanActivityRecognition/B-HAR_Baseline-Human-Activity-Recognition
https://github.com/BB-Repos/BBaction
Video Classification with Channel-Separated Convolutional Networks
https://github.com/BIG-S2/keras-gnm
https://github.com/BPMJG/annotated_pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Bamorovat/RHM_DualStreamC3D
https://github.com/Bamorovat/RHM_OneView
https://github.com/Bangbangbanana/r2plus1d_mindspore
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/BattashB/Adaptive-and-Iteratively-Improving-Recurrent-Lateral-Connections
Adaptive and Iteratively Improving Recurrent Lateral Connections
https://github.com/BehnooshParsa/HumanActionRecognition_with_ErgonomicRisk
Temporal Convolutional Networks for Action Segmentation and Detection
https://github.com/BelixRogner/SpeedChallenge
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/BenjiKCF/EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/BestJuly/IIC
Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action Recognition
https://github.com/BestJuly/VCP
Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning
https://github.com/BigLazyPig/Pytorch-StNet-Full-Implement
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/Blitzkrieg37/Kinesic
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
https://github.com/BlurryLight/DD-Net-Pytorch
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/BoChenUIUC/YOWO
https://github.com/BobMcDear/pytorch-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Burf/EfficientNet-Lite-Tensorflow2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/CheesyB/cpointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ChengBinJin/ActionViewer
Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN
https://github.com/ChengSashankh/trying-GAT
https://github.com/ChengyuSun/gat
https://github.com/ChetanTayal138/HAR-Web
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
https://github.com/ChintanThacker/Facial_Expression_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ChriXiang/GloRe_pytorch
https://github.com/ChrisWu1997/2D-Motion-Retargeting
Learning Character-Agnostic Motion for Motion Retargeting in 2D
https://github.com/Cloud-Computing-IoT/Cloud-Enabled-Smart-Speaker
https://github.com/Cloud-Computing-IoT/speakEasy
https://github.com/Cmeijiang/PointNet_MS
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.