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Latent Max-Margin Multitask Learning With Skelets for 3-D Action Recognition

https://doi.org/10.1109/tcyb.2016.2519448
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11744085_28openalex-snapshot
Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost
inferredcites →https://doi.org/10.1007/978-3-319-04561-0openalex-snapshot
Human Action Recognition with Depth Cameras
inferredcites →https://doi.org/10.1007/978-3-319-04561-0_3openalex-snapshot
Random Occupancy Patterns
inferredcites →https://doi.org/10.1007/s10994-007-5040-8openalex-snapshot
Convex multi-task feature learning
inferredcites →https://doi.org/10.1007/s11263-014-0703-yopenalex-snapshot
Weakly-Supervised Cross-Domain Dictionary Learning for Visual Recognition
inferredcites →https://doi.org/10.1007/s11263-015-0861-6openalex-snapshot
Kernelized Multiview Projection for Robust Action Recognition
inferredcites →https://doi.org/10.1016/j.ins.2014.01.025openalex-snapshot
Semantic preserving distance metric learning and applications
inferredcites →https://doi.org/10.1016/j.jvcir.2013.03.001openalex-snapshot
Effective 3D action recognition using EigenJoints
inferredcites →https://doi.org/10.1016/j.jvcir.2013.04.007openalex-snapshot
Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition
inferredcites →https://doi.org/10.1016/j.jvcir.2015.03.002openalex-snapshot
Joint movement similarities for robust 3D action recognition using skeletal data
inferredcites →https://doi.org/10.1016/j.patcog.2014.08.011openalex-snapshot
Accurate 3D action recognition using learning on the Grassmann manifold
inferredcites →https://doi.org/10.1023/a:1007379606734openalex-snapshot
Multitask Learning
inferredcites →https://doi.org/10.1038/scientificamerican0675-76openalex-snapshot
Visual Motion Perception
inferredcites →https://doi.org/10.1109/cvpr.2008.4587756openalex-snapshot
Learning realistic human actions from movies
inferredcites →https://doi.org/10.1109/cvpr.2011.5995316openalex-snapshot
Real-time human pose recognition in parts from single depth images
inferredcites →https://doi.org/10.1109/cvpr.2012.6247813openalex-snapshot
Mining actionlet ensemble for action recognition with depth cameras
inferredcites →https://doi.org/10.1109/cvpr.2013.123openalex-snapshot
An Approach to Pose-Based Action Recognition
inferredcites →https://doi.org/10.1109/cvpr.2014.121openalex-snapshot
Compact Representation for Image Classification: To Choose or to Compress?
inferredcites →https://doi.org/10.1109/cvpr.2014.335openalex-snapshot
Depth and Skeleton Associated Action Recognition without Online Accessible RGB-D Cameras
inferredcites →https://doi.org/10.1109/cvpr.2014.339openalex-snapshot
Cross-View Action Modeling, Learning, and Recognition
inferredcites →https://doi.org/10.1109/cvpr.2014.82openalex-snapshot
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
inferredcites →https://doi.org/10.1109/cvpr.2014.98openalex-snapshot
Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition
inferredcites →https://doi.org/10.1109/cvpr.2015.7298714openalex-snapshot
Hierarchical recurrent neural network for skeleton based action recognition
inferredcites →https://doi.org/10.1109/cvprw.2010.5543273openalex-snapshot
Action recognition based on a bag of 3D points
inferredcites →https://doi.org/10.1109/cvprw.2012.6239233openalex-snapshot
View invariant human action recognition using histograms of 3D joints
inferredcites →https://doi.org/10.1109/cvprw.2013.153openalex-snapshot
Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition
inferredcites →https://doi.org/10.1109/cvprw.2013.77openalex-snapshot
Recognizing Actions from Depth Cameras as Weakly Aligned Multi-part Bag-of-Poses
inferredcites →https://doi.org/10.1109/cvprw.2013.78openalex-snapshot
Fusing Spatiotemporal Features and Joints for 3D Action Recognition
inferredcites →https://doi.org/10.1109/iccv.2005.90openalex-snapshot
Exploring the space of a human action
inferredcites →https://doi.org/10.1109/iccv.2013.227openalex-snapshot
Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps
inferredcites →https://doi.org/10.1109/iccv.2013.281openalex-snapshot
Learning to Share Latent Tasks for Action Recognition
inferredcites →https://doi.org/10.1109/iccv.2013.333openalex-snapshot
Mining Motion Atoms and Phrases for Complex Action Recognition
inferredcites →https://doi.org/10.1109/iccv.2013.334openalex-snapshot
Learning Maximum Margin Temporal Warping for Action Recognition
inferredcites →https://doi.org/10.1109/iccv.2013.388openalex-snapshot
Latent Multitask Learning for View-Invariant Action Recognition
inferredcites →https://doi.org/10.1109/iccv.2013.58openalex-snapshot
Group Norm for Learning Structured SVMs with Unstructured Latent Variables
inferredcites →https://doi.org/10.1109/tcyb.2013.2265378openalex-snapshot
Enhanced Computer Vision With Microsoft Kinect Sensor: A Review
inferredcites →https://doi.org/10.1109/tcyb.2013.2273174openalex-snapshot
Spatio-Temporal Laplacian Pyramid Coding for Action Recognition
inferredcites →https://doi.org/10.1109/tcyb.2013.2276144openalex-snapshot
Computer vision for RGB-D sensors: Kinect and its applications [special issue intro.]
inferredcites →https://doi.org/10.1109/tcyb.2014.2350774openalex-snapshot
3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold
inferredcites →https://doi.org/10.1109/tcyb.2015.2399172openalex-snapshot
Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach
inferredcites →https://doi.org/10.1109/tcyb.2015.2414920openalex-snapshot
Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters
inferredcites →https://doi.org/10.1109/tii.2013.2255061openalex-snapshot
Rank Preserving Discriminant Analysis for Human Behavior Recognition on Wireless Sensor Networks
inferredcites →https://doi.org/10.1109/tip.2012.2199502openalex-snapshot
3-D Object Retrieval and Recognition With Hypergraph Analysis
inferredcites →https://doi.org/10.1109/tip.2012.2202676openalex-snapshot
Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search
inferredcites →https://doi.org/10.1109/tip.2014.2365699openalex-snapshot
Multitask Linear Discriminant Analysis for View Invariant Action Recognition
inferredcites →https://doi.org/10.1109/tkde.2009.191openalex-snapshot
A Survey on Transfer Learning
inferredcites →https://doi.org/10.1109/tnnls.2015.2458986openalex-snapshot
On the Performance of Manhattan Nonnegative Matrix Factorization
inferredcites →https://doi.org/10.1109/tpami.2009.167openalex-snapshot
Object Detection with Discriminatively Trained Part-Based Models
inferredcites →https://doi.org/10.1109/tpami.2013.2296528openalex-snapshot
Large-Margin Multi-ViewInformation Bottleneck
inferredcites →https://doi.org/10.1109/tpami.2015.2430325openalex-snapshot
Joint Binary Classifier Learning for ECOC-Based Multi-Class Classification
inferredcites →https://doi.org/10.1109/tpami.2015.2456899openalex-snapshot
Classification with Noisy Labels by Importance Reweighting
inferredcites →https://doi.org/10.1109/tpami.2015.2491925openalex-snapshot
Structure-Preserving Binary Representations for RGB-D Action Recognition
inferredcites →https://doi.org/10.1145/1553374.1553523openalex-snapshot
Learning structural SVMs with latent variables
inferredcites →https://doi.org/10.1145/1922649.1922653openalex-snapshot
Human activity analysis
inferredcites →https://doi.org/10.1145/2538028openalex-snapshot
A Regularization Approach to Learning Task Relationships in Multitask Learning
inferredcites →https://doi.org/10.1184/r1/6476414openalex-snapshot
Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity
inferredcites →https://doi.org/10.48550/arxiv.1205.2631openalex-snapshot
Multi-Task Feature Learning Via Efficient l2,1-Norm Minimization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Multifeature fusion action recognition based on key frames
inferred← citeshttps://doi.org/10.1007/978-3-030-68590-4_2openalex-snapshot
Skeleton-Based Activity Recognition: Preprocessing and Approaches
inferred← citeshttps://doi.org/10.1007/s00371-018-1489-7openalex-snapshot
Improving bag-of-poses with semi-temporal pose descriptors for skeleton-based action recognition
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Position constrained network for 3D human pose estimation
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Applying adversarial auto-encoder for estimating human walking gait abnormality index
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Exploring hybrid spatio-temporal convolutional networks for human action recognition
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Effective human action recognition using global and local offsets of skeleton joints
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A deep multimodal network based on bottleneck layer features fusion for action recognition
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Hierarchical topic modeling with pose-transition feature for action recognition using 3D skeleton data
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Deep Multi-View Feature Learning for Person Re-Identification
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Multi-Script-Oriented Text Detection and Recognition in Video/Scene/Born Digital Images
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Skeleton-Based Action Recognition With Gated Convolutional Neural Networks
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Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation
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Real-Time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model
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Doubly Approximate Nearest Neighbor Classification
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Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval
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Semantic Adversarial Network with Multi-Scale Pyramid Attention for Video Classification
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Deep View-Aware Metric Learning for Person Re-Identification
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BIAS-3D: Brain inspired attentional search model fashioned after what and where/how pathways for target search in 3D environment
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3D Behavior Recognition Based on Multi-Modal Deep Space-Time Learning
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Deep Learning of Fuzzy Weighted Multi-Resolution Depth Motion Maps with Spatial Feature Fusion for Action Recognition
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Deep Fusion of Skeleton Spatial–Temporal and Dynamic Information for Action Recognition
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Human Action Recognition via Depth Maps Body Parts of Action
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Vision and Sensor-Based Human Activity Recognition
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Learning Conditional Random Fields with Augmented Observations for Partially Observed Action Recognition
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Analysis and prediction of human behavior temporal sequences in the wild
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