https://github.com/GibranBenitez/IPN-hand
Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks
Papers with Code 아카이브
Activity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/GraphSAINT/GraphSAINT
https://github.com/HO4X/TSR_JetsonTX2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/HamzaGbada/GCN-Numpy
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/HardyYoungX/C3D
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/Harut0726/my-pointnet-tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/HazyResearch/hgcn
https://github.com/HeapHop30/graph-attention-nets
https://github.com/HelloDuoLA/refining-activation-downsampling-with-SoftPool
https://github.com/HobbitLong/PyContrast
https://github.com/HoganZhang/BSN-boundary-sensitive-network.pytorch
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/HoganZhang/pygcn_python3
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/HollrayChan/gradcam-plus-plus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/HuCui2022/JPFormer
https://github.com/HyeonhoonLee/MAIC2021_Sleep
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/HyperZealot/mxnet-cgan
https://github.com/IMvision12/keras-vision-models
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ISICV/ManTraNet
https://github.com/IdahoLabResearch/5GAD
https://github.com/IllinoisGraphBenchmark/IGB-Datasets
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/ImagingLab/Colorizing-with-GANs
https://github.com/InfoX-SEU/DAE-AQA
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/InfoX-SEU/DAE_AQA
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/IntelLabs/bayesian-torch
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
https://github.com/InwoongLee/TS-LSTM
https://github.com/IsaacPatole/DCGAN-and-CGAN
https://github.com/JJBOY/BMN-Boundary-Matching-Network
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/JUiscoming/Pytorch_GAN_MNIST
https://github.com/JZPeterPan/k-gin
https://github.com/JacobM184/EfficientNet-for-Gun-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/JacobYuan7/DIN-Group-Activity-Recognition-Benchmark
Spatio-Temporal Dynamic Inference Network for Group Activity Recognition
https://github.com/Jagyan/ghost_radarpoint_detection
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Jason-CKY/pokeGAN_fusion
https://github.com/Javed002/Object_detection_PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/JeffCHEN2017/WSSTG
https://github.com/JiaPeng1234/MRI-Segmentation-Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.