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- Hongchi ShiSuggested from co-authorship
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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.
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- 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.
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- 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.
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- 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.
- Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices100
This paper proposes an open framework to use the kernel matrix over feature dimensions as a generic representation and discusses its properties and advantages, which significantly elevates covariance representation to the unlimited opportunities provided by this new representation.
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- Investigation of different skeleton features for CNN-based 3D action recognition95
This paper proposes to encode five spatial skeleton features into images with different encoding methods and achieves state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis.
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- How computer vision can facilitate flood management: A systematic review87
A systematic review of relevant literature is presented and a need-based evaluation of computer vision's relative adequacy to specific needs associated with successive flood management phases is proposed.
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- Small‐molecule inhibition and activation‐loop trans‐phosphorylation of the IGF1 receptor82
The crystal structure of the tyrosine kinase domain of IGF1R (IGF1RK), in its unphosphorylated state, is presented, in complex with a novel compound, cis‐3‐[3‐(4‐methyl‐piperazin‐l‐yl]‐1‐(2‐phenyl‐quinolin‐7‐yl)‐imidazo[1,5‐a]pyrazin‐8‐ylamine (
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- An efficient iterative algorithm for image thresholding80
It is proven that the proposed algorithm for finding optimal thresholds that minimize a weighted sum-of-squared-error objective function is mathematically equivalent to the well-known Otsu's method, but requires much less computation.
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- Object detection using Non-Redundant Local Binary Patterns79
Experimental results show that the NRLBP is robust and adaptive with changes of the background and foreground and also outperforms the original LBP in detection task.
- Signaling Properties of a Non-metazoan Src Kinase and the Evolutionary History of Src Negative Regulation79
Choanoflagellates, unicellular organisms that are closely related to metazoans, possess cell adhesion and signaling proteins previously thought to be unique to animals, suggesting that these components may have played roles in the evolution of metazoan multicellularity.
- Revisiting Correlations between Intrinsic and Extrinsic Evaluations of Word Embeddings77
There are strong correlations between intrinsic and extrinsic evaluations, and the performance of different tasks can be affected by training corpora and context features to varying degrees.
- A crystallographic snapshot of tyrosine trans -phosphorylation in action74
The crystal structure of the FGF receptor 2 kinases caught in the act of trans-phosphorylation of Y769, the major C-terminal phosphorylation site, reveals that enzyme- and substrate-acting kinases engage each other through elaborate and specific interactions not only in the immediate vicinity of Y 769 and the enzyme active site, but also in regions that are as much of 18 Å away from D626.
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- Arousal modulates valence effects on both early and late stages of affective picture processing in a passive viewing task72
The current results suggest that the effect of emotional valence on affective picture perception is modulated by levels of arousal at both early and late stages of processing.
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- A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain69
A fusion framework to address the problem of under-detection of camouflaged moving foreground objects in the wavelet domain and demonstrates that the proposed method significantly outperformed existing methods in detecting camouflaged foreground objects.
- A Two-Stream Neural Network for Pose-Based Hand Gesture Recognition68
A two-stream neural network with one stream being a self-attention-based graph convolutional network (SAGCN) extracting the short-term temporal information and hierarchical spatial information, and the other being a residual-connection-enhanced bidirectional independently recurrent neural network (IndRNN) for extracting long- term temporal information.
- A Real‐Time Facial Expression Recognition System for Online Games68
An automatic expression recognition system that can be integrated into an MOG to control the facial expressions of avatars is proposed and a number of algorithms are studied, improved, and extended to meet the specific requirements.
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- A Central Difference Graph Convolutional Operator for Skeleton-Based Action Recognition60
An accelerated version of the CDGC is developed which greatly improves the speed of training and is not only able to aggregate node information like a vanilla graph convolutional operation but also gradient information.
- A Novel Self-Sequence Reaction Network Involving a Set of Six Reactions in One Pot: The Synthesis of Substituted Benzothiazoles from Aromatic Ketones and Anilines59
Using simple and readily available aromatic ketones and anilines as starting materials resulted in the construction of 2-acylbenzothiazoles via a novel self-sequence reaction network, which assembles six reactions in one pot.
- A novel shape-based non-redundant local binary pattern descriptor for object detection59
The experimental results have shown that the proposed descriptor can effectively describe non-rigid objects with high articulation and improve the detection rate compared to other state-of-the-art object descriptors.
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- Semi-Dynamic Hypergraph Neural Network for 3D Pose Estimation57
A novel Semi-Dynamic Hypergraph Neural Network (SD-HNN) is proposed to estimate 3D human pose from a single image to effectively exploit the kinematic constrains among adjacent and non-adjacent joints.
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- Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks56
This paper addresses the problem of continuous gesture recognition from sequences of depth maps using Convolutional Neural networks (ConvNets) and first segments individual gestures from a depth sequence based on quantity of movement (QOM).
- Fast characterization of biomass and waste by infrared spectra and machine learning models54
The robustness analysis was conducted by completely rearranging the training and test sets, and it further validated the hypothesis that the infrared spectra contains enough qualifying and quantifying information to characterize these properties of BW.
- Smoke Detection in Video: An Image Separation Approach54
Experimental results on synthesized and real video data have shown that the proposed approach can effectively separate the smoke component and the smoke detection performance is significantly improved by using the visual features extracted from the smoke components.
- Artificial intelligence‐enabled innovations in cochlear implant technology: Advancing auditory prosthetics for hearing restoration51
This comprehensive review explores the implications of artificial intelligence in addressing cochlear implant issues and revolutionizing the landscape of auditory prosthetics, and sets the stage for a transformative shift in auditory prosthetics, aiming to improve the quality of life for individuals with hearing loss.
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- Sharkeye: Real-Time Autonomous Personal Shark Alerting via Aerial Surveillance49
The Sharkeye platform was developed to provide swimmers and surfers with immediate information via smart apps, empowering lifeguards/lifesavers and beachgoers to prevent unwanted encounters with wildlife before it happens.
- Online human action recognition based on incremental learning of weighted covariance descriptors49
An effective framework of such an approach to online action recognition from a stream of noisy skeleton data, using a weighted covariance descriptor as a means to accumulate information is presented.
- Food image classification using local appearance and global structural information48
Experimental results verified that the combination of local appearance and structural features can improve classification performance and were evaluated on two datasets.
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- Mitochondrial Protein UCP1 Inhibits the Malignant Behaviors of Triple-negative Breast Cancer through Activation of Mitophagy and Pyroptosis46
It is suggested that UCP1 could inhibit the process of TNBC by activating mitophagy and pyroptosis, similar to the impairment of GSDME activation on the proliferation regulation of U CP1 on TNBC.
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- Mining Mid-Level Features for Action Recognition Based on Effective Skeleton Representation45
An effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition and this new representation yields state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D.
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- Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping42
An Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition are described.
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- Isolated Diastolic Hypertension and Risk of Cardiovascular Events: A Systematic Review and Meta-Analysis of Cohort Studies With 489,814 Participants40
Evidence is provided that IDH defined using the 2018 ESC criterion is significantly associated with an increased risk of composite cardiovascular events, cardiovascular mortality, all strokes and HS, but not significantly associatedwith all-cause death and IS.
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- Modeling Long-range Dependencies and Epipolar Geometry for Multi-view Stereo35
This article proposes a network, referred to as Multi-View Stereo TRansformer (MVSTR), capable of extracting dense features with global context and 3D consistency, which are crucial for reliable matching in multi-view stereo (MVS).
- A Hybrid Network for Large-Scale Action Recognition from RGB and Depth Modalities35
The paper presents a novel hybrid network that effectively leverages the strengths of the emerging Convolutional Neural Network and Recurrent Neural Network based approaches to specifically address the challenges that occur in large-scale action recognition and are not fully dealt with by the state-of-the-art methods.
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- Foreground detection in camouflaged scenes32
A texture guided weighted voting (TGWV) method which can efficiently detect foreground objects in camouflaged scenes and shows that the small and hardly noticeable differences between foreground and background in the image domain can be effectively captured in certain wavelet frequency bands.
- Temporal Dynamics of the Integration of Intention and Outcome in Harmful and Helpful Moral Judgment32
Significant ERP differences between attempted and successful actions over prefrontal and bilateral temporo-parietal regions were found in both harmful and helpful moral judgment, which suggest a possible time course of the integration processing in the brain.
- Transformer guided geometry model for flow-based unsupervised visual odometry30
A method consisting of two camera pose estimators that deal with the information from pairwise images and a short sequence of images, respectively that outperforms the state-of-the-art unsupervised learning-based methods by a large margin and performs comparably to supervised and traditional ones on the KITTI and Malaga dataset.
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- A Review of Dynamic Maps for 3D Human Motion Recognition Using ConvNets and Its Improvement27
A novel encoding scheme is developed that generates effective flow-guided dynamic maps, and they could select the high motion window and distinguish the order among the frames with small motion on the large Chalearn LAP IsoGD and NTU RGB+D datasets.
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- An effective edge-preserving smoothing method for image manipulation26
The method provides an unified framework to regularize the edge and texture pixels in the optimization so that geometric edges representing image structures can be well retained and fine edges of texture regions are removed or suppressed.
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- A Large Scale RGB-D Dataset for Action Recognition24
Four state-of-the-art algorithms are evaluated on the large combined dataset and the results have verified the limitations of current algorithms and the effectiveness of the large dataset.
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- Anti-corrosion superhydrophobic surface of LPBF- NiTi alloy fabricated by nanosecond laser machining21
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- SAR-NAS: Skeleton-based action recognition via neural architecture searching19
This paper encode a skeleton-based action instance into a tensor and carefully define a set of operations to build two types of network cells: normal cells and reduction cells, to search for an effective network architecture that is built upon the two kinds of cells.
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- Spatially and Temporally Structured Global to Local Aggregation of Dynamic Depth Information for Action Recognition18
This new representation, referred to as Spatially and Temporally Structured Dynamic Depth Images, aggregates from global to fine-grained levels motion and structure information in a depth sequence, and enables the existing ConvNet models trained on image data for classification of depth sequences, without a need for training the models afresh.
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- Efficient 2D viewpoint combination for human action recognition9
A novel method to efficiently combine 2D data from different viewpoints that not only combines the information from multiple viewpoints efficiently, but also improves the performance by mapping features into various kernel spaces.
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- Semantic and Spatial Content Fusion for Scene Recognition8
A novel framework to project image contextual feature space with semantic space of local features into a map function based on a subset of training images denoted as an exemplar-set that learns a weighted combination of local semantic topics as well as global and spatial information.
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- Study on the Risk Prediction of Real Estate Investment Whole Process Based on Support Vector Machine8
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- FT-HID: a large-scale RGB-D dataset for first- and third-person human interaction analysis7
A novel multi-view interaction mechanism for skeleton sequences, and a joint learning multi-stream framework for first-person and third-person visions are introduced, which yield promising results on the FT-HID dataset.
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- A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data4
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- Learning attentive dynamic maps (ADMs) for Understanding Human Actions2
A novel end-to-end trainable deep architecture to learn an attentive dynamic map (ADM) for understanding human motion from skeleton data achieved competitive performance compared with the state-of-the-art results on the Large Scale Combined dataset, MSRC-12 dataset and NTU RGB+D dataset.
- Discovering and Visualizing Underlying Traffic Regions from Vehicle Trajectories with Multi-Features2
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- Proceedings of the 4th International Symposium on Information, Computer, and Communications Security2
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- Land-use classification of Zhanghe river basin using the maximum likelihood and decision tree method1
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- Comprehensive evaluation model for the economic benefit of enterprises based on grey entropy and AHM1
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- Automatic Pricing and Replenishment Strategy for Vegetables Based on Multivariate Nonlinear Modeling–
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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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