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Claim this profileWorks441 from public data
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- Interfacial Microenvironment Modulation Boosting Electron Transfer between Metal Nanoparticles and MOFs for Enhanced Photocatalysis279
Systemic investigations suggest that the PVP presents a negative influence on the interfacial electron transfer between Pt and the MOF, and the removal of interfacial PVP effectively improves the sluggish kinetics of electron transfer, accordingly boosting the photocatalytic hydrogen production.
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- Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting196
This work proposes a novel lane detection method, whereby each lane has two boundaries, and demonstrates the outstanding performance of the method on the challenging dataset of road images compared with state-of-the-art lane-detection methods.
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- Person Re-Identification with Correspondence Structure Learning175
A boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair is introduced, which integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images.
- A cloud image detection method based on SVM vector machine157
A cloud image detection method based on SVM vector machine to remove thick cloud data to reduce the amount of data to improve the efficiency of the data.
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- Adaptive Exploration for Unsupervised Person Re-identification150
An Adaptive Exploration (AE) method to address the domain-shift problem for re-ID in an unsupervised manner and integrates a balance strategy into the adaptive selection.
- MDSSD: multi-scale deconvolutional single shot detector for small objects145
A Multi-scale Deconvolutional Single Shot Detector (MDSSD), especially for small object detection, which achieves state-of-the-art results for general object detection on PASCAL VOC2007 test and MS COCO test-dev2015, especially achieving 2 to 5 points improvement on small object categories.
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- Crowd Behavior Simulation With Emotional Contagion in Unexpected Multihazard Situations122
This paper proposes a novel crowd simulation method by modeling the generation and contagion of panic emotion under multihazard circumstances, and introduces an emotional reciprocal velocity obstacles (RVOs) model to simulate the crowd behaviors by augmenting the traditional RVO model with emotional contagion.
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- High quality multi-spectral and panchromatic image fusion technologies based on Curvelet transform117
Based on the experiments on proposed algorithms, the comparison used the five image fusion evaluation standard is given and the synthetic image, image information entropy, relatively non dimensional global error (ERGAS) and universal image quality index UIQI all better than other images in three bands, and the average gradient is optimal in two bands.
- Propane oxidative dehydrogenation over highly selective hexagonal boron nitride catalysts: The role of oxidative coupling of methyl113
In this work, it is reported that the reaction pathways over the h-BN catalyst are different from the V-based catalysts in ODHP, and sheds light on the selective oxidation of alkanes such as direct upgrading of methane via oxidative upgrading to ethylene or CHxOy on boron- based catalysts.
- 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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- Margin & diversity based ordering ensemble pruning97
This research proves that the ensemble pruning method should focus more on the following two factors: example margin and ensemble diversity, and proposes a novel metric called the margin & diversity based measure (MDM) to explicitly evaluate the importance of individual classifiers.
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- A novel combined model based on echo state network for multi-step ahead wind speed forecasting: A case study of NREL85
A novel combined model for wind speed forecasting, which combined hybrid models based on decomposition method and optimization algorithm, and ESN (Echo state network) is initially applied to integrate all the results obtained by each hybrid model and achieve the ultimate forecasting results.
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- Depth Information Guided Crowd Counting for complex crowd scenes80
This paper proposes a Depth Information Guided Crowd Counting (DigCrowd) method that first uses the depth information of an image to segment the scene into a far-view region and a near- view region and introduces a new crowd dataset that contains 1000 images.
- Crowd Simulation and Its Applications: Recent Advances80
The state-of-the-art crowd simulation techniques and their selected applications are surveyed and viewpoints on open crowd simulation research challenges are offered and point out potential future directions in this field.
- A survey of urban visual analytics: Advances and future directions78
This work comprehensively review urban visual analytics studies from four perspectives, identifying 8 urban domains and 22 types of popular visualization, analyze 7 types of computational method, and categorize existing systems into 4 types based on their integration of visualization techniques and computational models.
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- Towards Better Bus Networks: A Visual Analytics Approach77
This work proposes a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network and incorporates a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one.
- SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction76
This article proposes one new prediction model named social soft attention graph convolution network (SSAGCN), which aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments and achieves state-of-the-art results.
- BANet: Bidirectional Aggregation Network With Occlusion Handling for Panoptic Segmentation74
A novel deep panoptic segmentation scheme based on a bidirectional learning pipeline is proposed and a plug-and-play occlusion handling algorithm is introduced to deal with the occlusions between different object instances.
- Pornographic image detection utilizing deep convolutional neural networks73
To detect pornographic images in any style accurately and efficiently with a single model, a novel scheme utilizing the deep convolutional neural networks (CNN) is proposed and it is shown that this method lead to state-of-the-art detection performance.
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- Interactive mechanism modeling from multi-view images71
An interactive system for mechanism modeling from multi-view images that generates 3D mechanism models that contain not only geometric shapes but also internal motion structures that can be directly animated through kinematic simulation.
- LG-CNN: From local parts to global discrimination for fine-grained recognition70
This paper proposes a jointly trained Convolutional Neural Network (CNN) architecture to solve the fine-grained recognition problem without using part and bounding box information and applies this architecture to generic object recognition datasets with superior performance over the directly fine-tuned CNN architecture.
- Weighted bilateral K-means algorithm for fast co-clustering and fast spectral clustering69
A novel weighted bilateral k-means (WBKM) algorithm is proposed and applies it for co-clustering and fast spectral clustering and has faster computational speed, which achieves more promising clustering results.
- A novel wind speed forecasting model based on moving window and multi-objective particle swarm optimization algorithm66
A novel combined model based on multi-objective particle swarm optimization, which is applied to optimize the key parameters of the echo state network is proposed, which performs better than the combined model that has been proposed before.
- Short-term wind speed forecasting system based on multivariate time series and multi-objective optimization62
An ensemble system that considers historical wind speed series and other weather factors to make wind speed forecasting and performs better than other comparative models in terms of four performance indicators is proposed.
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- Learning Correspondence Structures for Person Re-Identification62
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification by introducing a boosting-based approach to learn a correspondence structure, which indicates the patchwise matching probabilities between images from a target camera pair and introduces a global constraint-based matching process.
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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.
- FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring59
A lightweight and real-time unsupervised BID baseline, termed Frequency-domain Contrastive Loss Constrained Lightweight CycleGAN (shortly, FCL-GAN), with attractive properties, i.e., no image domain limitation, no image resolution limitation, 25x lighter than Sota, and 5x faster than SOTA is proposed.
- A Tube-and-Droplet-Based Approach for Representing and Analyzing Motion Trajectories59
This paper addresses the problem of representing motion trajectories in a highly informative way, and utilizes the droplet-based process for analyzing trajectories and applies it to trajectory analysis applications including trajectory clustering, trajectory classification & abnormality detection, and 3D action recognition.
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- VIS+AI: integrating visualization with artificial intelligence for efficient data analysis57
The highest integration level of visualization and AI is described as the framework of VIS+AI, which allows AI to learn human intelligence from interactions and communicate with humans through visual interfaces.
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- Memory-Augmented Relation Network for Few-Shot Learning57
This work investigates a new metric-learning method to explicitly exploit its relationships with the others in the working context, and formulate the distance metric as a learnable relation module which learns to compare for similarity measurement, and equip theWorking context with memory slots, both contributing to generality.
- 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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- Multi-label correlation guided feature fusion network for abnormal ECG diagnosis55
The main objective of this paper is to design an ECG abnormal event detection model based on multi-label correlation guided feature fusion, and the proposed method achieves better results compared with other state-of-the-art methods.
- Dual Distribution Alignment Network for Generalizable Person Re-Identification53
This paper proposes an end-to-end Dual Distribution Alignment Network (DDAN) to learn domain-invariant features with dual-level constraints: the domain-wise adversarial feature learning and the identity-wise similarity enhancement.
- Protected Resource Allocation in Space Division Multiplexing-Elastic Optical Networks with Fluctuating Traffic53
Experimental results demonstrate that the proposed algorithm can achieve promising performance with the peak load of the fluctuating traffic, and the Triangular Iterative Core Assignment (TICA) strategy to overcome XT, and to minimize the blocking probability.
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- Collective Crowd Formation Transform with Mutual Information–Based Runtime Feedback52
This approach combines both macroscopic and microscopic controls of the crowd transformation to maximally maintain subgroups' local stability and dynamic collective behaviour, while minimizing the overall effort of the agents during the transformation.
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- User-Guided Personalized Image Aesthetic Assessment Based on Deep Reinforcement Learning50
This work proposes a novel user-guided personalized image aesthetic assessment framework that leverages user interactions to retouch and rank images for aesthetic assessment based on deep reinforcement learning (DRL), and generates personalized aesthetic distribution that is more in line with the aesthetic preferences of different users.
- Adversarial co-distillation learning for image recognition50
This paper proposes a novel approach named Adversarial Co-distillation Networks (ACNs) to enhance the “dark knowledge” by generating extra divergent examples and designs “Weakly Residual Connection” module and “Restricted Adversaria Search’ module to assist in the training process.
- miSFM: On combination of Mutual Information and Social Force Model towards simulating crowd evacuation50
The key innovation lies in highlighting how the dynamic adjustment of SFM parameters reveals much more realistic crowd movements for the evacuation simulation.
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- ART-UP: A Novel Method for Generating Scanning-Robust Aesthetic QR Codes48
A new method to generate scanning-robust aesthetic QR codes is proposed, which is based on a module-based scanning probability estimation model that can effectively balance the tradeoff between visual quality and scanning robustness.
- Emotion-Based Crowd Simulation Model Based on Physical Strength Consumption for Emergency Scenarios48
To the best of the knowledge, this model is the first method integrating physical strength consumption into an emotion-based crowd simulation model by exploiting the relationship betweenPhysical strength consumption and emotion.
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- Joint Attribute Manipulation and Modality Alignment Learning for Composing Text and Image to Image Retrieval47
The proposed method, Composing Text and Image to Image Retrieval (CTI-IR), which aims at retrieving images relevant to a query image with text describing desired modifications to the query image is devoted to.
- Ultraflexible Neural Electrodes Enabled Synchronized Long-Term Dopamine Detection and Wideband Chronic Recording Deep in Brain44
The electrodes were implanted into the brains of rodent models to monitor the changes in neural and electrochemical signals across different brain regions during the administration of nomifensine and highlighted the high quality and stability of the electrophysiological/electrochemical codetection neural electrodes.
- USAR44
A novel and user-friendly aesthetic ranking framework via powerful deep neural network and a small amount of user interaction, which can automatically estimate and rank the aesthetic characteristics of images in accordance with users' preference.
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- Two-stream network for infrared and visible images fusion42
This paper proposes an endto- end model (TSFNet) for infrared and visible image fusion, which is able to handle the sources simultaneously and adopts an adaptive weight allocation strategy to capture the informative global features.
- Cascaded Revision Network for Novel Object Captioning42
This paper proposes a novel network structure, Cascaded Revision Network, which aims at relieving the problem of inaccurate captioning by equipping the model with out-of-domain knowledge and can accurately describe images with unseen objects.
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- ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data40
This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series based on prediction models, and designs a drift level index to depict the dynamics, and a consistency judgment model to justify whether the conceptdrifts from various sources are consistent.
- Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects39
A joint attention-guided feature fusion network (JAFFNet) for saliency detection of surface defects based on the encoder–decoder network that learns to emphasize defect features and suppress background noise during feature fusion, which is beneficial for detecting low-contrast defects.
- Revisiting Local Descriptor for Improved Few-Shot Classification38
A new method, named DCAP, is presented, in which it is shown that the reliance on sophisticated classifiers is not necessary, and a simple classifier applied directly to improved feature embeddings can instead outperform most of the leading methods in the literature.
- Reinforcement Learning-Based Visual Navigation With Information-Theoretic Regularization38
This work introduces an information-theoretic regularization term into the RL objective of target-driven visual navigation based on deep reinforcement learning (RL) that maximizes the mutual information between navigation actions and visual observation transforms of an agent, thus promoting more informed navigation decisions.
- A bundled-optimization model of multiview dense depth map synthesis for dynamic scene reconstruction38
This paper proposes a bundled-optimization scheme to process the thorough chain from capture to multiview dense depth map generation for the 3D applications, and proposes a Blocklet based depth map optimization method for this blurring effect, and the accuracy of the high resolution depth map is then improved.
- Noise Self-Regression: A New Learning Paradigm to Enhance Low-Light Images Without Task-Related Data37
NoiSER without access to any task-related data, simply learns a convolutional neural network equipped with an instance-normalization layer by taking a random noise image and feeding it to the trained network for predicting the normal-light image.
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- A Survey on Concept Factorization: From Shallow to Deep Representation Learning37
This survey provides an insightful overview of both theoretical basis and current developments in the field of CF, which can help the interested researchers to understand the current trends of CF and find the most appropriate CF techniques to deal with particular applications.
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- A Natural-language-based Visual Query Approach of Uncertain Human Trajectories36
This paper proposes a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data, built up on encoding massive, spatially uncertain trajectories by the semantic information of the POls and regions covered by them and storing the trajectory documents in text database with an effective indexing scheme.
- Self-Supervised Point Cloud Representation Learning via Separating Mixed Shapes33
A new self-supervised learning method, called Mixing and Disentangling (MD), for 3D point cloud representation learning, which has improved the empirical performance on both ModelNet-40 and ShapeNet-Part datasets in terms of point cloud classification and segmentation tasks.
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- Personalized training through Kinect-based games for physical education33
A novel technique that helps the Kinect-based training system to select the subsequential training material for the users according to their realtime performance is proposed and the results show that the proposed technique enhances the effect of physical training significantly.
- Crowd queuing simulation with an improved emotional contagion model33
This research studies the emotional contagion in crowd queuing from psychological and social computing perspectives and proposes an emotional contagions method for crowd simulation that combines an improved susceptible-infected-recovered (SIR) model with cellular automata.
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- 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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- Locality preserving dense graph convolutional networks with graph context-aware node representations21
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- Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion19
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- Dimensionality reduction of data sequences for human activity recognition17
The framework of manifold elastic net is introduced that encodes the local geometry to find an aligned coordinate system for data representation and is efficient because classification error minimization criterion is utilized to directly link the classification error with the selected subspace.
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- Improving Vision Anomaly Detection With the Guidance of Language Modality12
This paper proposes Cross-modal Guidance (CMG), comprising of Cross-modal Entropy Reduction (CMER) and Cross-modal Linear Embedding (CMLE), to address the issues of redundant information and sparse latent space, respectively.
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- Learning Efficient GANs using Differentiable Masks and co-Attention Distillation.9
A novel GAN compression method, termed DMAD, is introduced, by proposing a Differentiable Mask and a co-Attention Distillation that simultaneously distills informative attention maps from both the generator and discriminator of a pre-trained model to the searched generator, effectively stabilizing the adversarial training of the light-weight model.
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- Handwriting posture prediction based on unsupervised model9
This paper proposes a neural network constructed with small convolution kernels to extract features from handwriting, and incorporate unsupervised learning and handwriting data analysis to predict writing postures and achieves an accuracy rate significantly higher than the human experts.
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- Logic Distillation: Learning from Code Function by Function for Decision-making Tasks8
Experiments demonstrate that with the assistance of LD, S-LLMs can achieve outstanding results in continuous decision-making tasks, comparable to, or even surpassing, those of L-LLMs.
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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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- Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering6
This work presents the bilaterally slimmable Transformer (BST), a general framework that can be seamlessly integrated into arbitrary Transformer-based VQA models to train a single model once and obtain various slimmed submodels of different widths and depths.
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- Improving large models with small models: Lower costs and better performance5
DS+ not only substantially reduces the cost associated with querying large models but also effectively improves large models' performance, and experiments prove that the proposed collaborative-based paradigm can better inject specific task knowledge into PLMs compared to fine-tuning.
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- Delegate-based Utility Preserving Synthesis for Pedestrian Image Anonymization4
A new delegate-based utility preserving synthesis (DUPS) approach for pedestrian image anonymization that can not only achieve superior anonymization performance against deep pedestrian recognizers, but also can obtain a better tradeoff between privacy protection and utility preservation compared with state-of-the-art methods.
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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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