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/chaoyuaw/pytorch-coviar
https://github.com/charlesq34/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/chenxuluo/GST-video
Grouped Spatial-Temporal Aggregation for Efficient Action Recognition
https://github.com/chihina/Joint-Group-Activity-Recognition
https://github.com/chohoseong/Transformer-based-Unified-Recognition-of-Two-Hands-Manipulating-Objects
https://github.com/chrisqqq123/FA-Dist-EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/christiansafka/img2vec
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/chunfeng0301/Flow
https://github.com/cioppaanthony/context-aware-loss
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
https://github.com/ckyrkou/EmergencyNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/clic-lab/chalet
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/clic-lab/drif
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/clin366/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/clip-italian/clip-italian
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/clip-vil/cider
https://github.com/clovaai/rexnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/cmsflash/efficient-attention
https://github.com/coconutzs/PointNet_zs
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/code-implementation1/Code1/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/code-implementation1/Code2/tree/main/3dcnn
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/code-implementation1/Code2/tree/main/AdderNGD
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/code-implementation1/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/code-implementation1/Code6/tree/main/Non_local
https://github.com/code-implementation1/Code6/tree/main/r2plus1d
https://github.com/coderSkyChen/Action_Recognition_Zoo
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/colincsl/TemporalConvolutionalNetworks
Temporal Convolutional Networks for Action Segmentation and Detection
https://github.com/comp-well-org/Data_Augmentation_for_Biobehavioral_Time_Series_Data
Transformer Networks for Data Augmentation of Human Physical Activity Recognition
https://github.com/conceptofmind/Region-ViT-flax
RegionViT: Regional-to-Local Attention for Vision Transformers
https://github.com/concordiaca/3D-ResNets-PyTorch
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/congvm-it/pyciderevalcap
https://github.com/coolbay/VSGN
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/corcovadoming/mpnnel
https://github.com/craftGBD/caffe-GBD
https://github.com/craston/MARS
https://github.com/croros/STAR_Network_Pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.