Papers with Code 아카이브
Action Recognition In Videos
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Action Recognition in Videos is a task in computer vision and pattern recognition where the goal is to identify and categorize human actions performed in a video sequence. The task involves analyzing the spatiotemporal dynamics of the actions and mapping them to a predefined set of action classes, such as running, jumping, or swimming.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
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Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/jerryljq/ActionRecognition
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/jihun-kr/SlowFast
https://github.com/jonmun/EPIC-KITCHENS-100_UDA_TA3N
https://github.com/khu-vll/cast
CAST: Cross-Attention in Space and Time for Video Action Recognition
https://github.com/labs12/Action-Recgontion-
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/lahaproject/inavit
Interaction Region Visual Transformer for Egocentric Action Anticipation
https://github.com/leftthomas/r2plus1d-c3d
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/hiera
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/liu824/representation-flow-for-action-recognition-paddlepaddle
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/mcgridles/LENS
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/metalbubble/TRN-pytorch
https://github.com/microsoft/unicl
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/research/cv/tsn
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/mtlouie-unm/alome-tsn
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
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Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/nhannguyen2709/video_recognition
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/nikankind/Reproduce-Article-Representation-Flow-for-Action-Recognition-with-PaddlePaddle
https://github.com/niveditarahurkar/CS231N-ActionRecognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/nuschandra/Tennis-Stroke-Detection
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
https://github.com/okankop/MFF-pytorch
https://github.com/open-mmlab/mmaction
https://github.com/open-mmlab/mmaction2
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/peachman05/action-recognition-tutorial
https://github.com/phoenix1327/tea-action-recognition
TEA: Temporal Excitation and Aggregation for Action Recognition
https://github.com/piergiaj/latent-subevents
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
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