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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/1adrianb/video-transformers
공식 코드https://github.com/2020aptx4869lm/Forecasting-Human-Object-Interaction-in-FPV
공식 코드https://github.com/2tianyao1/actionllm
공식 코드https://github.com/ADiko1997/S-GEAR
공식 코드Semantically Guided Representation Learning For Action Anticipation
https://github.com/BestJuly/Inter-intra-video-contrastive-learning
공식 코드Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
https://github.com/Chiaraplizz/ST-TR
공식 코드Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks
https://github.com/CorcovadoMing/HORST
공식 코드Higher Order Recurrent Space-Time Transformer for Video Action Prediction
https://github.com/HimangiM/RepLAI
공식 코드Learning State-Aware Visual Representations from Audible Interactions
https://github.com/MCG-NJU/TDN
공식 코드TDN: Temporal Difference Networks for Efficient Action Recognition
https://github.com/NyleSiddiqui/MultiView_Actions
공식 코드DVANet: Disentangling View and Action Features for Multi-View Action Recognition
https://github.com/OpenGVLab/VideoMAEv2
공식 코드VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
https://github.com/PanasonicConnect/ego4d_lta_ens_srw_seqdep_verb_noun_cooc
공식 코드Technical Report for Ego4D Long Term Action Anticipation Challenge 2023
https://github.com/ParitoshParmar/HalluciNet
공식 코드HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/Soumyabrata2003/Simba
공식 코드Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos
https://github.com/VisionLearningGroup/R-C3D
공식 코드R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
https://github.com/ahmed-nady/multimodal-action-recognition
공식 코드EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action Recognition
https://github.com/ai-forever/ghost
공식 코드A new face swap method for image and video domains: a technical report
https://github.com/alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates
공식 코드Multi-Temporal Convolutions for Human Action Recognition in Videos
https://github.com/alexandrosstergiou/Squeeze-and-Recursion-Temporal-Gates/blob/master/train_model.py
공식 코드Learn to cycle: Time-consistent feature discovery for action recognition
https://github.com/anshulbshah/PoseAction
공식 코드https://github.com/antran89/two-stream-fcan
공식 코드Two-stream Flow-guided Convolutional Attention Networks for Action Recognition
https://github.com/aras62/SF-GRU
공식 코드Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs
https://github.com/assembly101/assembly101.github.io
공식 코드Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
https://github.com/bhi-research/ava_mdetr
공식 코드Exploring Modulated Detection Transformer as a Tool for Action Recognition in Videos
https://github.com/bomri/SlowFast/blob/master/projects/vtn/README.md
공식 코드https://github.com/brown-palm/AntGPT
공식 코드AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?
https://github.com/brown-palm/ObjectPrompt
공식 코드Object-centric Video Representation for Long-term Action Anticipation
https://github.com/dandoge/palm
공식 코드https://github.com/decisionforce/VTHCL
공식 코드https://github.com/deepmind/deepmind-research/tree/master/mmv
공식 코드https://github.com/dibschat/tempAgg
공식 코드Temporal Aggregate Representations for Long-Range Video Understanding
https://github.com/dongzhikang/mambavl
공식 코드https://github.com/dun-research/haltingvt
공식 코드HaltingVT: Adaptive Token Halting Transformer for Efficient Video Recognition
https://github.com/epic-kitchens/epic-kitchens-100-annotations
공식 코드https://github.com/epic-kitchens/epic-kitchens-100-narrator
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.