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/hildekuehne/Weak_YouTube_dataset
https://github.com/hiyouga/fastedit
https://github.com/hjjpku/adaptive_sampler
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/hnVfly/pointnet.mxnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/hnmspirit/pointnet1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/hobincar/SA-LSTM
https://github.com/houshd/LSTM-FCN
https://github.com/houstonsantos/CassavaLeafDisease
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/huggingface/pytorch-image-models
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/huggingface/transformers
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/huguyuehuhu/HCN-pytorch
https://github.com/huynhtuan17ti/AI4VN-Hackathon2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/huyz1117/Non_Local_Net_TensorFlow
https://github.com/huzpsb/EffTransfer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hyang0129/foodclassapp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hyperfraise/Pytorch-StNet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/hyperfraise/StNet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/hyperfraise/end-to-end-BSN.pytorch
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/hyperparameters/tracking_via_colorization
https://github.com/hz-ants/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/iamhoushiyou/tsn
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/iamilyasedunov/key_word_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/iamjeff7/j-va-aagcn
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
https://github.com/ibrahimalimetin/HDAD-iGAV-Dataset-and-its-Benchmarking-GUI-with-Deep-Learning-Techniques-for-HAR
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
https://github.com/iduta/pyconvsegnet
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/iejMac/ScriptWriter
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/if-loops/adaptive-selective-synaptic-dampening
Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening
https://github.com/imanlab/deep_movement_primitives
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/innat/UniFormerV2
https://github.com/innat/Video-FocalNets
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
https://github.com/innat/VideoMAE
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/innat/VideoSwin
https://github.com/isaachaw/GrabCarRecognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/isotlaboratory/ml4vrp
https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.