https://github.com/3dperceptionlab/visual-wetlandbirds
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
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/404nofound/gat
https://github.com/4pygmalion/GAT_tensorflow
https://github.com/AI-Guru/pointcloud_experiments
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/AbdurrahmanNadi/activity_recognition_web_service
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/AbiterVX/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/AgrawalAmey/safe-explorer
https://github.com/AlbertoSabater/Domain-and-View-point-Agnostic-Hand-Action-Recognition
https://github.com/AlexandraFilimokhina/MountainCarConrinious_DDPG
https://github.com/Alexyuda/action_recognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/AlfredoZermini/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Alpha-Video/AlphaVideo
https://github.com/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Anak2016/GAT
https://github.com/AndreiMoraru123/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Andrew-booler/BSN-ActivityNet
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/AngusMonroe/GAT-pytorch
https://github.com/Anou9531/GAT
https://github.com/Aveek-Saha/Graph-Attention-Net
https://github.com/B-HAR-HumanActivityRecognition/B-HAR_Baseline-Human-Activity-Recognition
https://github.com/BIG-S2/keras-gnm
https://github.com/BPMJG/annotated_pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Bamorovat/RHM_DualStreamC3D
https://github.com/Bamorovat/RHM_OneView
https://github.com/BehnooshParsa/HumanActionRecognition_with_ErgonomicRisk
Temporal Convolutional Networks for Action Segmentation and Detection
https://github.com/BlurryLight/DD-Net-Pytorch
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/BoChenUIUC/YOWO
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
https://github.com/Brook1711/RIS_components
https://github.com/CAMMA-public/cholect45
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/CheesyB/cpointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ChengBinJin/ActionViewer
Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN
https://github.com/ChengSashankh/trying-GAT
https://github.com/ChengyuSun/gat
https://github.com/ChetanTayal138/HAR-Web
https://github.com/Cmeijiang/PointNet_MS
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.