https://github.com/BoPang1996/Semi-Coupled-Structure-for-visual-sequental-tasks
공식 코드Complex Sequential Understanding through the Awareness of Spatial and Temporal Concepts
Papers with Code 아카이브
Action Recognition
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BobXWu/AKEW
공식 코드https://github.com/Buzz-Beater/LEMMA
공식 코드LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities
https://github.com/CAMMA-public/cholect45
공식 코드Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
https://github.com/CAMMA-public/cholect50
공식 코드https://github.com/CAMMA-public/cholectriplet2021
공식 코드CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
공식 코드https://github.com/CMU-CREATE-Lab/video-labeling-tool
공식 코드https://github.com/CV-Lehigh/Bias_IMD
공식 코드https://github.com/CVIR/TCL
공식 코드Semi-Supervised Action Recognition with Temporal Contrastive Learning
https://github.com/CVPRUSFTampa/EventSegmentation
공식 코드A Perceptual Prediction Framework for Self Supervised Event Segmentation
https://github.com/Chiaraplizz/ST-TR
공식 코드Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks
https://github.com/Cogito2012/DEAR
공식 코드https://github.com/CorcovadoMing/HORST
공식 코드Higher Order Recurrent Space-Time Transformer for Video Action Prediction
https://github.com/DAVEISHAN/TCLR
공식 코드TCLR: Temporal Contrastive Learning for Video Representation
https://github.com/DaphneAntotsiou/task-oriented-hand-retargeting
공식 코드Task-Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation
https://github.com/Darcyddx/Video-LLM
공식 코드https://github.com/DeLightCMU/ElaborativeRehearsal
공식 코드https://github.com/DirtyHarryLYL/DJ-RN
공식 코드Detailed 2D-3D Joint Representation for Human-Object Interaction
https://github.com/DirtyHarryLYL/HAKE-Action-Torch
공식 코드HAKE: A Knowledge Engine Foundation for Human Activity Understanding
https://github.com/DrMahdiRezaei/DeepSOCIAL
공식 코드DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
https://github.com/EHZ9NIWI7/MSF-GZSSAR
공식 코드Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action Recognition
https://github.com/ENSCMA2/flextape
공식 코드"Flex Tape Can't Fix That": Bias and Misinformation in Edited Language Models
https://github.com/EdoWhite/Gate-Shift-Pose
공식 코드Gate-Shift-Pose: Enhancing Action Recognition in Sports with Skeleton Information
https://github.com/Efstathia-Soufleri/PKD
공식 코드Advancing Compressed Video Action Recognition through Progressive Knowledge Distillation
https://github.com/Ekim-Yurtsever/DeepTL-Lane-Change-Classification
공식 코드https://github.com/FingerRec/Self-Supervised-Temporal-Discriminative-Representation-Learning-for-Video-Action-Recognition
공식 코드Self-supervised Temporal Discriminative Learning for Video Representation Learning
https://github.com/Gauffret/TrajectorySet
공식 코드Revisiting hand-crafted feature for action recognition: a set of improved dense trajectories
https://github.com/Gbouna/RE-TCN
공식 코드Action Recognition in Real-World Ambient Assisted Living Environment
https://github.com/GinnyXiao/ReSpike
공식 코드ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition
https://github.com/HCIS-Lab/Action-slot
공식 코드https://github.com/HJYao00/Side4Video
공식 코드Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer Learning
https://github.com/Heliot7/open-set-da
공식 코드https://github.com/HimangiM/RepLAI
공식 코드Learning State-Aware Visual Representations from Audible Interactions
https://github.com/HobbitLong/CMC
공식 코드https://github.com/Howal/DNL-Object-Detection
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.