https://github.com/marcointrovigne/WeatherDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Papers with Code 아카이브
Activity Recognition
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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/marload/ImprovedResNet-TensorFlow2
https://github.com/marload/TensorFlow2-GANs
https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch
https://github.com/maxwelltsai/DeepGalaxy
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mcg-nju/trace
Action Genome: Actions as Composition of Spatio-temporal Scene Graphs
https://github.com/md-mohaiminul/ViS4mer
https://github.com/md-mohaiminul/objectstatechange
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/mdeff/paper-cnn-graph-nips2016
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/megvii-research/basecls/tree/main/zoo/public/effnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/meliketoy/graph-cnn.pytorch
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/memory-attention-networks/MANs
Memory Attention Networks for Skeleton-based Action Recognition
https://github.com/mengxingshifen1218/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/mengyuest/AR-Net
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
https://github.com/merazlab/3D_Deep_Learning_Link
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/mertyg/post-hoc-cbm
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/metalbubble/TRN-pytorch
https://github.com/metalbubble/moments_models
Moments in Time Dataset: one million videos for event understanding
https://github.com/metra4ok/MLSTM-FCN-Pytorch
https://github.com/michi-3000/eyeclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/microsoft/computervision-recipes
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/microsoft/qlib/tree/main/examples/benchmarks/ADARNN
https://github.com/microsoft/unicl
https://github.com/mikecheninoulu/Emotional-gesture-papers
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild
https://github.com/miladvazan/LDA-MLP-SVM-SGD
https://github.com/mindspore-ai/models/blob/master/research/cv/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ai/models/blob/master/research/cv/glore_res
https://github.com/mindspore-ai/models/blob/master/research/cv/glore_res200
https://github.com/mindspore-ai/models/tree/master/official/cv/C3D/src
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/mindspore-ai/models/tree/master/official/gnn/gat
https://github.com/mindspore-ai/models/tree/master/research/cv/CGAN
https://github.com/mindspore-ai/models/tree/master/research/cv/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/mindspore-ai/models/tree/master/research/cv/r2plus1d
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/mindspore-ai/models/tree/master/research/cv/stnet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/mindspore-ai/models/tree/master/research/cv/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/mindspore-ai/models/tree/master/research/cv/tsn
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.