https://github.com/evm7/ego4dlta-icvae
공식 코드Intention-Conditioned Long-Term Human Egocentric Action Forecasting
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.
https://github.com/facebookarchive/C3D
공식 코드Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/facebookresearch/AVT
공식 코드https://github.com/facebookresearch/SlowFast
공식 코드https://github.com/facebookresearch/hiera
공식 코드Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/facebookresearch/memvit
공식 코드MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition
https://github.com/facebookresearch/vjepa2
공식 코드V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
https://github.com/feichtenhofer/st-resnet
공식 코드Spatiotemporal Residual Networks for Video Action Recognition
https://github.com/feichtenhofer/twostreamfusion
공식 코드Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/fpv-iplab/easg
공식 코드Action Scene Graphs for Long-Form Understanding of Egocentric Videos
https://github.com/fpv-iplab/rulstm
공식 코드https://github.com/google-research/google-research/tree/master/vatt
공식 코드VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/guoxih/Busy-Quiet-Video-Disentangling-for-Video-Classification
공식 코드https://github.com/guoxih/region-based-non-local-network
공식 코드https://github.com/guxiao0822/trans_action
공식 코드TransAction: ICL-SJTU Submission to EPIC-Kitchens Action Anticipation Challenge 2021
https://github.com/hikvision-research/cost
공식 코드Collaborative Spatio-temporal Feature Learning for Video Action Recognition
https://github.com/kahnchana/svt
공식 코드https://github.com/kevin-ssy/Optical-Flow-Guided-Feature
공식 코드Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
https://github.com/kracwarlock/action-recognition-visual-attention
공식 코드https://github.com/leonlha/Video-Action-Recognition-via-PSO-ConvNet-Transformer-Collaborative-Learning-with-Dynamics
공식 코드Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer
https://github.com/leonlha/video-action-recognition-collaborative-learning-with-dynamics-via-pso-convnet-transformer
공식 코드Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer
https://github.com/linuxsino/arr
공식 코드From Recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation
https://github.com/martinetoering/ViCC
공식 코드Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
https://github.com/mks0601/IntegralAction_RELEASE
공식 코드IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos
https://github.com/mondalanindya/msqnet
공식 코드Actor-agnostic Multi-label Action Recognition with Multi-modal Query
https://github.com/monjurulkarim/DSTA
공식 코드A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents
https://github.com/naraysa/gzsl-od
공식 코드Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition
https://github.com/nmegha2601/anticipatr
공식 코드Rethinking Learning Approaches for Long-Term Action Anticipation
https://github.com/okankop/Efficient-3DCNNs
공식 코드https://github.com/olga-zats/goal_consistency
공식 코드https://github.com/olga-zats/gtda
공식 코드Gated Temporal Diffusion for Stochastic Long-Term Dense Anticipation
https://github.com/olivesgatech/DARai
공식 코드Hierarchical and Multimodal Data for Daily Activity Understanding
https://github.com/opengvlab/egoexolearn
공식 코드https://github.com/opengvlab/egovideo
공식 코드EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation
https://github.com/park-jungin/dualpath
공식 코드https://github.com/piergiaj/representation-flow-cvpr19
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.