https://github.com/Nortinwell/TSN
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Papers with Code 아카이브
Temporal Action Localization
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Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/OanaIgnat/i3d_keras
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Ontheway361/BSN_pytorch
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/OpenXAIProject/LRP_for_LSTM_Korean_dataset
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
https://github.com/PPPrior/i3d-pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/PaParaZz1/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/PaParaZz1/TemporalShiftModule
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PASSL
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/PaddlePaddle/PaddleClas
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/localization/bmn.md
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsn.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/stgcn.md
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/video/models/bsn
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/PaddlePaddle/models/tree/develop/dygraph/bmn
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/ParitoshParmar/C3D-LSTM--PyTorch
https://github.com/ParitoshParmar/HalluciNet--PyTorch
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/ParitoshParmar/LTSOE
https://github.com/PeterWang512/FALdetector
https://github.com/Phimos/Paddle-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/PolyArch/fp-diannao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Powercoder64/TAA-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/PumpkinYing/GAT
https://github.com/Puzer/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
https://github.com/RonyAbecidan/Mantranet-pytorch
https://github.com/RoshanRane/segmentation-moving-MNIST
https://github.com/RudreshVeerkhare/StyleGan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Run542968/ProCL
Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning
https://github.com/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Ruturaj123/VGG16-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Ryosaeba8/Anomaly_detection
https://github.com/SBPL-Cruz/perch_pose_sampler
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/STAIR-Lab-CIT/STAIR-actions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.