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/dvl-tum/mot_neural_solver
https://github.com/dvl-tum/motsynth-baselines
https://github.com/dwtstore/sfm1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/dzb1998/pyGAT
https://github.com/dzwallkilled/IEforAR
Investigation of Different Skeleton Features for CNN-based 3D Action Recognition
https://github.com/ebarsoum/hpgan
https://github.com/ehijano/Faster-RCNN-Global-Wheat-Detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ehsannowroozi/RDFS
https://github.com/eknim/gradcamplusplus
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/elbuco1/AttentionMechanismsTrajectoryPrediction
What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction
https://github.com/eliasboughosn/Spatial-Temporal-Graph-Convolutional-Networks
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/elliott-iadvize/ocr-aws-distributed
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/emartinezs44/SparkGCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/enriccorona/human-motion-prediction-pytorch
https://github.com/eric-mitchell/serac
https://github.com/ericksiavichay/cs230-final-project
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/escorciav/Text-to-Clip_Retrieval
Guess Where? Actor-Supervision for Spatiotemporal Action Localization
https://github.com/escorciav/roi_pooling
Guess Where? Actor-Supervision for Spatiotemporal Action Localization
https://github.com/eth-ait/spl
https://github.com/eth212/Qmind_ModelNet10_PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ethanluoyc/transporter-pytorch
Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/fabioasdias/ubao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/facebookresearch/QuaterNet
https://github.com/facebookresearch/SparseConvNet
https://github.com/facebookresearch/VMZ
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/facebookresearch/detectron
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/facebookresearch/fairseq
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
https://github.com/facebookresearch/xformers
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
https://github.com/facundoq/transformational_measures_experiments
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fandulu/Keras-for-Co-occurrence-Feature-Learning-from-Skeleton-Data-for-Action-Recognition
https://github.com/fangwei123456/spikingjelly
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
https://github.com/fanzhenliu/dagad
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/fchollet/deep-learning-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fdsa1860/skeletal
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.