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Works507 from public data
- 1,488
- The genome of the choanoflagellate Monosiga brevicollis and the origin of metazoans1,138
It is shown that the physical linkages among protein domains often differ between M. brevicollis and metazoans, suggesting that abundant domain shuffling followed the separation of the choanoflagellate and metazoan lineages.
- Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN937
It is shown that an IndRNN can be easily regulated to prevent the gradient exploding and vanishing problems while allowing the network to learn long-term dependencies and work with non-saturated activation functions such as relu and be still trained robustly.
- Joint Geometrical and Statistical Alignment for Visual Domain Adaptation598
A unified framework that reduces the shift between domains both statistically and geometrically, referred to as Joint Geometrical and Statistical Alignment (JGSA) is proposed.
- The default mode network and social understanding of others: what do brain connectivity studies tell us463
Brain connectivity results related to the Default Mode Network in the fields of social understanding of others: emotion perception, empathy, theory of mind, and morality indicate that the DMN is indispensable in the socialUnderstanding of others.
- Importance Weighted Adversarial Nets for Partial Domain Adaptation461
This paper proposes a novel adversarial nets-based partial domain adaptation method to identify the source samples that are potentially from the outlier classes and, at the same time, reduce the shift of shared classes between domains.
- RGB-D-based human motion recognition with deep learning: A survey401
A detailed overview of recent advances in RGB-D-based motion recognition is presented, and the methods of encoding spatial-temporal-structural information inherent in video sequence are highlighted, and potential directions for future research are discussed.
- Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks389
A compact, effective yet simple method to encode spatio-temporal information carried in 3D skeleton sequences into multiple 2D images, referred to as Joint Trajectory Maps (JTM), and ConvNets are adopted to exploit the discriminative features for real-time human action recognition.
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- RGB-D-based action recognition datasets: A survey282
A comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi- view datasets, and 7 multi-person datasets, resulting in a number of recommendations for collection of new datasets and use of evaluation protocols.
- Human detection from images and videos: A survey268
A comprehensive survey on the recent development and challenges of human detection in the thread of human object descriptors is provided, providing a thorough analysis of the state-of-the-art human detection methods and a guide to the selection of appropriate methods in practical applications.
- Action recognition based on joint trajectory maps with convolutional neural networks237
An effective yet simple method to represent spatio-temporal information carried in 3 D skeleton sequences into three 2 D images by encoding the joint trajectories and their dynamics into color distribution in the images, referred to as Joint Trajectory Maps (JTM).
- Skeleton-based action recognition using LSTM and CNN192
The proposed method achieved state-of-the-art results on NTU RGB+D datasets for 3D human action analysis and ranked 1st place in Large Scale 3D Human Activity Analysis Challenge in Depth Videos.
- Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks182
The image-based representations enable the existing ConvNet models trained on image data without training a large number of parameters from scratch, and achieve the state-of-art results on three large datasets, namely, the large-scale continuous gesture recognition dataset.
- 168
- Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks151
A new representation, namely, Scene Flow to Action Map (SFAM), that describes several long term spatio-temporal dynamics for action recognition from RGB-D data and takes better advantage of the trained ConvNets models over ImageNet.
- 137
- Robust unsupervised feature selection via dual self-representation and manifold regularization135
Experimental results on ten real-world data sets demonstrate that the proposed method can effectively identify important features, outperforming many state-of-the-art unsupervised feature selection methods in terms of clustering accuracy (ACC) and normalized mutual information (NMI).
- Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition119
This survey categorises the cross-dataset recognition into 17 problems based on a set of carefully chosen data and label attributes to provide a systematic approach and a reference for a machine-learning practitioner to categorise a real problem and to look up for a possible solution accordingly.
- Trear: Transformer-Based RGB-D Egocentric Action Recognition107
A transformer-based RGB-D egocentric action recognition framework that adopts a self-attention mechanism to model the temporal structure of the data from different modalities and outperforms the state-of-the-art results by a large margin.
- 106
- ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring103
Through transferring learning the models originally trained over ImageNet for image classification, the three ConvNets are trained independently on the color-coded DMMs constructed in three orthogonal planes, achieving state-of-the-art results on these datasets.
- Security modeling and efficient computation offloading for service workflow in mobile edge computing101
This paper proposes a security and energy efficient computation offloading (SEECO) strategy for service workflows in MEC environment, the goal of which is to optimize the energy consumption under the risk probability and deadline constraints.
- Large-scale Isolated Gesture Recognition using Convolutional Neural Networks100
A convolutional Neural networks (ConvNets) based method is developed for gesture recognition and evaluated on the Large-scale Isolated Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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