Papers with Code 아카이브
Action Detection
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Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Sheldonmao/Learning-To-walk
https://github.com/ShobhitMaheshwari/sign-language1
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/SilvioGiancola/SoccerNet-code
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
https://github.com/SimonRamstedt/ddpg
https://github.com/SmartPorridge/google-AVA-Dataset-downloader
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/SonbolYb/multivariate_timeseries_dilated_conv
Multivariate Time Series Classification using Dilated Convolutional Neural Network
https://github.com/SonuDileep/3-D-Object-Detection-using-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Souphis/mobile_robot_rl
https://github.com/StanLei52/GEBD
Generic Event Boundary Detection: A Benchmark for Event Segmentation
https://github.com/StanfordVL/RubiksNet
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/StefOe/indrnn-pytorch
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/Stepphonwol/my_yowo
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
https://github.com/Subrata11/IoT-based-Real-time-Human-Motion-Recognition-Based-on-Skeletons-
https://github.com/Sun1992/SSTCN-for-SLR
https://github.com/Sunnydreamrain/IndRNN
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/Sy-Zhang/Geometric-Feature-Release
https://github.com/T3chy/DDPG
https://github.com/THHHomas/mls
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/TaatiTeam/STARS
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences
https://github.com/TaatiTeam/stgcn_parkinsonism_prediction
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Taeuk-Jang/pointcompletion
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Tencent/ActionDetection-DBG
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/TheInfamousWayne/ddpg
https://github.com/TobiasLee/Text-Classification
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/TsingZ0/FL-IoT
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
https://github.com/Tudouu/stgcn_light_op
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/TyngJiunKuo/deep-learning-project
https://github.com/VaanHUANG/CSCI5210HW1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/VasaKiDD/TD3-deep-rl-research
https://github.com/VisualAnalysisOfHumans/LOVEU_TRACK1_TOP3_SUBMISSION
Generic Event Boundary Detection: A Benchmark for Event Segmentation
https://github.com/WangChen0902/BSN
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/WangYueFt/dgcnn
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/WantingZhao/my_GAT
https://github.com/WenwanChen/pvad
https://github.com/WittmannF/quadcopter-best-practices
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.