On the public citation graph

Towards 3D Human Action Recognition Using a Distilled CNN Model

https://doi.org/10.1109/siprocess.2018.8600485

0 signed · 28 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

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

inferredcites →https://doi.org/10.1002/cav.1782openalex-snapshot

3D skeleton‐based action recognition by representing motion capture sequences as 2D‐RGB images

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.patcog.2017.02.030openalex-snapshot

Enhanced skeleton visualization for view invariant human action recognition

inferredcites →https://doi.org/10.1016/j.patcog.2017.10.013openalex-snapshot

Recent advances in convolutional neural networks

inferredcites →https://doi.org/10.1109/acpr.2015.7486569openalex-snapshot

Skeleton based action recognition with convolutional neural network

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.2014.82openalex-snapshot

Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group

inferredcites →https://doi.org/10.1109/cvpr.2016.115openalex-snapshot

NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis

inferredcites →https://doi.org/10.1109/cvpr.2016.484openalex-snapshot

Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data

inferredcites →https://doi.org/10.1109/cvpr.2016.514openalex-snapshot

DisturbLabel: Regularizing CNN on the Loss Layer

inferredcites →https://doi.org/10.1109/cvpr.2017.137openalex-snapshot

Deep Learning on Lie Groups for Skeleton-Based Action Recognition

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.2013.76openalex-snapshot

Joint Angles Similarities and HOG2 for Action Recognition

inferredcites →https://doi.org/10.1109/dicta.2015.7371223openalex-snapshot

A Survey on Human Action Recognition Using Depth Sensors

inferredcites →https://doi.org/10.1109/iccv.2017.115openalex-snapshot

Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks

inferredcites →https://doi.org/10.1109/taslp.2014.2339736openalex-snapshot

Convolutional Neural Networks for Speech Recognition

inferredcites →https://doi.org/10.1109/tvcg.2010.272openalex-snapshot

Learning a 3D Human Pose Distance Metric from Geometric Pose Descriptor

inferredcites →https://doi.org/10.1109/wacv.2013.6474999openalex-snapshot

Berkeley MHAD: A comprehensive Multimodal Human Action Database

inferredcites →https://doi.org/10.1109/wacv.2017.24openalex-snapshot

On Geometric Features for Skeleton-Based Action Recognition Using Multilayer LSTM Networks

inferredcites →https://doi.org/10.1145/1599470.1599473openalex-snapshot

Efficient and robust annotation of motion capture data

inferredcites →https://doi.org/10.1145/2398356.2398381openalex-snapshot

Real-time human pose recognition in parts from single depth images

inferredcites →https://doi.org/10.5220/0004718301220130openalex-snapshot

Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks

inferredcites →https://doi.org/10.5244/c.25.67openalex-snapshot

Does Human Action Recognition Benefit from Pose Estimation?

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4654397openalex-extracted

Harcnn: A Union of 3d Convolutional Neural and Recurrent Neural Network for Human Action Recognition

inferred← citeshttps://doi.org/10.3390/app132011199openalex-snapshot

Reconstructed Prototype Network Combined with CDC-TAGCN for Few-Shot Action Recognition

inferred← citeshttps://doi.org/10.3390/books978-3-0365-1624-0openalex-snapshot

Advanced Topics in Systems Safety and Security

inferred← citeshttps://doi.org/10.3390/info10120365openalex-snapshot

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.