Papers with Code 아카이브
Action Localization
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Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gpspelle/Crowd-Counting
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/griffinbran/machines_best_friend
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/gsig/charades-algorithms
https://github.com/guide2157/ChulaXrayClassifier
What Makes Training Multi-Modal Classification Networks Hard?
https://github.com/guiggh/hand_pose_action
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
https://github.com/guilk/diva
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/gulvarol/ltc
https://github.com/gulvarol/surreact
Synthetic Humans for Action Recognition from Unseen Viewpoints
https://github.com/gurkirt/realtime-action-detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/haaz77/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/handasontam/GAT-with-edgewise-attention
https://github.com/hangzhaomit/HACS-dataset
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal Localization
https://github.com/hariharannatesh/Action-Recognition-using-Pytorch-Geometric
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/harlanhong/MM2021-CO2-Net
Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization
https://github.com/harunshimanto/Neural-Style-Transfer-of-Artistic-Style
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hassony2/kinetics_i3d_pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/hazdzz/ChebyNet
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/hazdzz/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/hazdzz/SGC
https://github.com/hcygeorge/aoi_defect_detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hehefan/P4Transformer
https://github.com/hehefan/PSTNet2
https://github.com/helloxy96/CS5242_Project2020
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/henriquemeloo/cat-dog-image-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hhe-distance/AIF-CNN
https://github.com/hi-archers/stableke
MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
https://github.com/hieu2906090/deep-learning-in-js
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/highwaywu/imageforensicsosn
https://github.com/hildekuehne/Weak_YouTube_dataset
https://github.com/hiyouga/fastedit
https://github.com/hnVfly/pointnet.mxnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/hnmspirit/pointnet1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/hobincar/SA-LSTM
https://github.com/hollance/VGGNet-Metal
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/holstgr-kaust/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.