https://github.com/Jintao-Huang/EfficientNet_PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Papers with Code 아카이브
Activity Recognition
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- 11
- 평가표
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- 보존된 평가 기록
- 10
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/Jmak12/Iris1
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/JoegameZhou/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/JoelLeupp/Conv3D-Shot-Boundary-Detection
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/JojiJoseph/DCGAN
https://github.com/JojiJoseph/GAN
https://github.com/JuliBaCSE/WeCare_makeathon
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/JunweiLiang/aicity_action
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
https://github.com/KSonPham/ViVit-a-Pytorch-implementation
https://github.com/KaidongLi/tf-3d-alpha
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Kaimaoge/IGNNK
https://github.com/KaiyangZhou/mixstyle-release
https://github.com/Kaushalya/medclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/KennCoder7/Recurrent-Attention-Network-for-HAR
https://github.com/KhusDM/PointNetTree
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/KiUngSong/Vision
https://github.com/KimMeen/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/KingGugu/I3D
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/KiranAkadas/My_Pointnet_v2
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/KrisLee512/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Kristof-Neys/GAT_Neo4j_DGL
https://github.com/LEChaney/Real-time-SSAR
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
https://github.com/LONG-9621/Extract_Point_3D
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/LONG-9621/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/LRacoci/permutation-graphml
https://github.com/LarryJiang134/Image_manipulation_detection
https://github.com/LebronGG/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Lechatelia/SSN
https://github.com/Lechatelia/action-proposal-generation
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/Legoons/Melanoma_classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/LijieFan/tvnet
End-to-End Learning of Motion Representation for Video Understanding
https://github.com/LiliMeng/3D-ResNets-PyTorch
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/LiliMeng/3D-ResNets-Pytorch-ImageNet-Moments
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/Linardos/federated-HCM-diagnosis
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular Disease
https://github.com/Liut2016/stackedResBiLSTM
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
https://github.com/Lornatang/CGAN-PyTorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.