https://github.com/luciferbobo/dae-aqa
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 4
- 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/lukecavabarrett/pna
https://github.com/lukemelas/EfficientNet-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lunaproject22/rpa
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/luoweizhou/coco-caption
https://github.com/luuchung/cifar-100
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lvapeab/coco-caption
https://github.com/lvweiwolf/efficientdet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lvyufeng/MindSpore-GAN
https://github.com/lyqcom/cgan
https://github.com/lyqcom/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/m-bain/video-transformers
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/mHealthBuet/SegCodeNet
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/machinelearning-goettingen/EfficientNet-ModelScaling
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/madrylab/pretraining-distribution-shift-robustness
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/magnux/DMNN
https://github.com/maharshi95/Pose2vec
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
https://github.com/mahendrathapa/graph-convolution-network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahequn001/graphlearning/tree/master/model_zoo/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahsa91/GKD
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mahsa91/RA-GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/mainaksingha01/applenet
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mainaksingha01/odg-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mamonraab/Data-efficient-video-transformer
https://github.com/mamtajha-ts/gesture-recognition
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/mangalutsav/Multi-Stage-LSTM-for-Action-Anticipation
https://github.com/manish-vi/Human-Activity-Recognition
Understanding and Improving Deep Neural Network for Activity Recognition
https://github.com/mansimov/unsupervised-videos
https://github.com/manu-vishwakarma/human-activity-detection
Understanding and Improving Deep Neural Network for Activity Recognition
https://github.com/maragori/DeepfakeForensics-v1
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/marble0117/GNN_models_pytorch
https://github.com/marble0117/GNN_models_pytorch_geometric
https://github.com/marblet/GNN_models_pytorch_geometric
https://github.com/marblet/gat-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.