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Works375 from public data
- Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering758
A Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi- modal features, which results in superior performance for VQA compared with other bilinear pooling approaches.
- Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering532
A DNN architecture is designed to integrate all these aforementioned modules into a unified model for achieving superior VQA performance and an ensemble of the authors' MFH models achieve the state-of-the-art performance on the large-scale VQ a data sets and win the runner-up in V QA Challenge 2017.
- Seeded region growing: an extensive and comparative study318
This work designs an automatic SRG algorithm, along with a boundary-oriented parallel pixel labeling technique and an automatic seed selection method, and a seed tracking algorithm is proposed for automatic moving object extraction.
- Boundary-aware context neural network for medical image segmentation261
A boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information, which incorporates encoder-decoder architecture.
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- Towards mobility-based clustering185
Experimental results show that using 0.3% of vehicles as the samples, mobility-based clustering can accurately identify hot spots which can hardly be obtained by the latest representative algorithm UMicro.
- Fusion method for infrared and visible images by using non-negative sparse representation181
Compared with the classical and state-of-the-art methods, the experimental results have indicated that the NNSP method has better fusion performance in both noiseless and noisy situations.
- Topic oriented community detection through social objects and link analysis in social networks166
A topic oriented community detection approach which combines both social objects clustering and link analysis, which can achieve a better performance when the topics are at least as important as the links to the analysis.
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- Multi-level annotation of natural scenes using dominant image components and semantic concepts124
This paper proposes a multi-level approach to annotate the semantics of natural scenes by using both the dominant image components (salient objects) and the relevant semantic concepts to achieve automatic image annotation at the content level.
- Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers114
A hierarchical boosting algorithm is proposed by incorporating feature hierarchy and boosting to scale up SVM image classifier training in high-dimensional feature space and its experiments on a specific domain of natural images have obtained very positive results.
- NeXtVLAD: An Efficient Neural Network to Aggregate Frame-Level Features for Large-Scale Video Classification111
A fast and efficient network architecture, NeXtVLAD, to aggregate frame-level features into a compact feature vector for large-scale video classification, which turns out to be both effective and parameter efficient in aggregating temporal information.
- Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment107
An adaptive fractional dilated convolution (AFDC) is developed, which is aspect-ratio-embedded, composition-preserving and parameter-free, which can be easily implemented by common deep learning libraries and plugged into popular CNN architectures in a computation-efficient manner.
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- Ranking influential nodes in social networks based on node position and neighborhood97
This paper proposes a multi-attribute ranking method based on node position and neighborhood, with low computational complexity O(n), which can efficiently and accurately provide a more reasonable ranking list than previous approaches.
- Hierarchical classification for automatic image annotation92
A novel hyperbolic visualization framework is seamlessly incorporated to enable intuitive query specification and evaluation by acquainting the users with a good global view of large-scale image collections.
- Semantic Image Browser: Bridging Information Visualization with Automated Intelligent Image Analysis91
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- Exploring video content structure for hierarchical summarization86
A hierarchical video summarization scheme is introduced by executing various approaches at different levels of the video content hierarchy to statically or dynamically construct the video summary.
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- Automatic image annotation by using concept-sensitive salient objects for image content representation82
A multi-level approach to annotate the semantics of natural scenes by using both the dominant image components and the relevant semantic concepts and an adaptive EM algorithm has been proposed to determine the optimal model structure and model parameters simultaneously.
- Hierarchical video content description and summarization using unified semantic and visual similarity82
A hierarchical video content description and summarization strategy supported by a novel joint semantic and visual similarity strategy is presented, and experiments on real-word videos have validated the effectiveness of the proposed approach.
- Statistical modeling and conceptualization of natural images81
An adaptive EM algorithm has been proposed to determine the optimal model structure and model parameters simultaneously and a large number of unlabeled samples have been integrated with a limited number of labeled samples to achieve more effective classifier training and knowledge discovery.
- A Novel Machine Learning Method Based Approach for Li-Ion Battery Prognostic and Health Management74
A deep learning method that combines the Forgetting Online Sequential Extreme Learning Machine (FOS-ELM) with the Hybrid Grey Wolf Optimizer (HGWO) algorithm and attention mechanism for the Prognostic and Health Management of Li-ion battery has higher efficiency and accuracy.
- Multi-level video content represntation and retrieval69
A novel technique is presented, which can adapt the threshold for scene cut detection to the activities of variant videos or even different video shots, and is proposed to support more efficient multilevel video representation, summarization, indexing, and access techniques.
- Dual regularized multi-view non-negative matrix factorization for clustering68
A novel algorithm, called Dual-regularized Multi-view Non-negative Matrix Factorization (DMvNMF), is developed for multi-view data clustering, which is able to preserve the geometric structures of multi- view data in both the data space and the feature space.
- Jointly Learning Visually Correlated Dictionaries for Large-Scale Visual Recognition Applications68
A joint dictionary learning (JDL) algorithm which exploits the inter-category visual correlations to learn more discriminative dictionaries to be computationally affordable in large-scale applications is presented.
- Parameter estimation in quantum sensing based on deep reinforcement learning67
This work proposes a physical-inspired linear time-correlated control ansatz and a general well-defined reward function integrated with the derived bounds to accelerate the network training for fast generating quantum control signals and evaluates the transferability of the scheme when the parameter has a shift from the true parameter.
- Semisupervised image classification by mutual learning of multiple self‐supervised models65
This work proposes a two‐stage semisupervised learning framework that enhances each of the two SSMs with the complementary information provided by the other such that the correct prediction could be shared.
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- Hierarchical learning of multi-task sparse metrics for large-scale image classification63
A novel approach is developed to learn a tree of multi-task sparse metrics hierarchically over a visual tree to achieve a fast solution to large-scale image classification, where an enhanced visual tree is first learned to organize large numbers of image categories hierarchically in a coarse-to-fine fashion.
- Quick attribute reduction in inconsistent decision tables62
Judgment theorems for the assignment reduct, the distribution reduct and the maximum distribution reduCT are proposed, which are expected to greatly simplify the judging of these three types of reducts in inconsistent decision tables.
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- Practical advantage of quantum machine learning in ghost imaging56
This work investigates the practical advantage of quantum machine learning in ghost imaging by overcoming the limitations of classical methods in blind object identification and imaging and proposes two hybrid quantum-classical machine learning algorithms and a physical-inspired patch strategy to allow distributed quantum learning with parallel variational circuits.
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- A generalized least-squares approach regularized with graph embedding for dimensionality reduction54
The PCA (Principal Component Analysis) idea of minimizing least-squares reconstruction errors is regularized with graph embedding, to unify various local manifold embedding methods within a generalized framework to keep global and local low dimensional subspace.
- Hierarchical clustering algorithm for categorical data using a probabilistic rough set model54
A new technique called TMDP (Total Mean Distribution Precision) is proposed for selecting the partitioning attribute based on probabilistic rough set theory and it is shown that the MTMDP algorithm can be successfully used to analyze grouped categorical data because it produces better clustering results.
- Integrating multi-level deep learning and concept ontology for large-scale visual recognition53
The experimental results on three image sets have demonstrated that the multi-level deep learning algorithm can achieve very competitive results on both the accuracy rates and the computational efficiency for large-scale visual recognition.
- An improved automatic isotropic color edge detection technique51
An improved automatic edge detection technique that can provide more reasonable results as compared with the traditional isotropic edge operators, its calculation cost has been reduced and good balance between the calculation cost and the edge detection accuracy is achieved.
- A hierarchical access control model for video database systems50
A novel approach to support multilevel access control in video databases by combining a video database indexing mechanism with a hierarchical organization of visual concepts, and presents an innovative access control model which is able to support flexible multileVEL access control to video elements.
- Effective De-identification Generative Adversarial Network for Face Anonymization49
A novel effective de-identification generative adversarial network (DeIdGAN) for face anonymization by seamlessly replacing a given face image with a different synthesized yet realistic one to achieve better identity protection rates and preserve superior image quality and data reusability.
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- Image Clustering with External Guidance48
An externally guided clustering method (Text-Aided Clustering, TAC), which leverages the textual semantics of WordNet to facilitate image clustering, which achieves state-of-the-art performance on five widely used and three more challengingimage clustering benchmarks, including the full ImageNet-1K dataset.
- Leveraging loosely-tagged images and inter-object correlations for tag recommendation48
A multi-task structured SVM algorithm is developed to leverage both the inter-object correlations and the loosely-tagged images for achieving more effective training of a large number of inter-related object classifiers.
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- Cost-sensitive learning of hierarchical tree classifiers for large-scale image classification and novel category detection47
A cost-sensitive hierarchical learning algorithm developed to train hierarchical tree classifiers for large-scale image classification application can achieve very competitive results on both classification accuracy and computational efficiency as compared with other state-of-the-art techniques.
- A novel approach for privacy-preserving video sharing46
A distributed framework for privacy-preserving classifier training is developed, able to significantly reduce the costs of data transmission and reliably limit the privacy breaches by determining the optimal size of blurred test samples for classifier validation.
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- A novel approach to enable semantic and visual image summarization for exploratory image search45
A novel scheme to incorporate topic network and representativeness-based sampling for achieving semantic and visual summarization and visualization of large-scale collections of Flickr images and experiments on large- scale image collections with diverse semantics have provided very positive results.
- Performance analysis and optimization of MPI collective operations on multi-core clusters42
A new parallel computation model to unitedly abstract memory hierarchy on multi-core clusters in vertical and horizontal levels is proposed, which provides the theoretical underpinning for the optimal design of MPI collective operations.
- Support cluster machine42
The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies, and provides a promising approach to dealing with privacy-preserving data mining problems.
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- Incorporating feature hierarchy and boosting to achieve more effective classifier training and concept-oriented video summarization and skimming39
A novel scheme to incorporate the results of semantic video classification to select the most representative video shots for generating concept-oriented summarization and skimming of surgery education videos is developed and a novel multi-modal boosting algorithm is developed to achieve more reliable video classifier training.
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- Image sequence segmentation based on 2D temporal entropic thresholding34
Experimental results show that this segmentation-based coding scheme is more efficient than normal fixed-size coding algorithms.
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- Incorporating concept ontology to enable probabilistic concept reasoning for multi-level image annotation33
A novel framework to incorporate the concept ontology to achieve hierarchical probabilistic image concept reasoning for multi-level image annotation is proposed and shown that it is scalable to diverse image contents with large within-category variations.
- Contour-enhanced CycleGAN framework for style transfer from scenery photos to Chinese landscape paintings32
A contour-enhancing translation branch is designed, which effectively guides the transfer from photos to paintings with edge detection operators computing the gradient maps, and indicates that the introduction of the edge operator makes the generated landscape paintings more aesthetic, especially in situations where scenery photos are rich in edge information.
- Class-specific object proposals re-ranking for object detection in automatic driving31
This paper proposes a semantic, class-specific approach to re-rank object proposals, which can consistently improve the recall performance even with fewer proposals, and can achieve high recall rate under strict critical even using fewer proposals.
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- Self-Supervised Graph Neural Network for Multi-Source Domain Adaptation28
A novel Self-Supervised Graph Neural Network (SSG) is proposed, where a graph neural network is used as the bridge to enable more effective inter-task information exchange and knowledge sharing and more expressive representation is learned by adopting a mask token strategy to mask some domain information.
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- ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos24
ANetQA is presented, a large-scale benchmark that supports fine-grained compositional reasoning over the challenging untrimmed videos from ActivityNet and is an order of magnitude larger than AGQA with a similar number of videos.
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- Deep embedding of concept ontology for hierarchical fashion recognition23
A new hierarchical approach is developed for large-scale fashion recognition that embeds concept ontology into deep convolutional neural network by adopting multiple deep CNN branches to learn node-specific features and classifiers explicitly and introduces a hierarchical knowledge distillation method to further improve the performance of fashion recognition.
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- A Novel Approach for Filtering Junk Images from Google Search Results19
A kernel-based image clustering technique to partition the images returned by Google Images into multiple visually-similar clusters is developed and a novel framework is developed to achieve more intuitive visualization of large amount of returned images according to their visual similarity.
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- ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research16
ORMind is introduced, a cognitive-inspired framework that enhances optimization through counterfactual reasoning that emulates human cognition, implementing an end-to-end workflow that systematically transforms requirements into mathematical models and executable solver code.
- ZS-SRT: An efficient zero-shot super-resolution training method for Neural Radiance Fields16
A zero-shot super-resolution training framework for NeRF that does not consume high-resolution views or additional scene data to train super-resolution NeRF and can speed up the training process by adopting a coarse-to-fine strategy.
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- Deep Mixture of Diverse Experts for Large-Scale Visual Recognition15
A deep mixture of diverse experts algorithm is developed to achieve more efficient learning of a huge (mixture) network for large-scale visual recognition application and can achieve very competitive results on large- scale visual recognition.
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- Integrating multi-modal content analysis and hyperbolic visualization for large-scale news video retrieval and exploration12
A novel scheme to achieve more effective analysis, retrieval and exploration of large-scale news video collections by performing multi-modal video content analysis and synchronization and a novel hyperbolic visualization scheme is incorporated to visualize large- scale news topics according to their associations and interestingness.
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- PAINT: Photo-realistic Fashion Design Synthesis11
Experimental results demonstrate that the proposed novel Photo-reAlistic fashIon desigN synThesis (PAINT) framework is capable of generating diverse photo-realistic multi-view fashion design images with fine-grained appearance details conditioned on the provided multiple inputs.
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- Large scale news video database browsing and retrieval via information visualization7
A novel framework is developed to enable more effective visual analysis and retrieval of large-scale news videos via interactive visualization, so that the audiences can find news stories of interest at first glance.
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- New Approach for Hierarchical Classifier Training and Multi-level Image Annotation6
A novel hierarchical boosting algorithm is proposed by incorporating concept ontology and multi-task learning to achieve hierarchical image classifier training to enable automatic multi-level image annotation.
- Large-scale news video retrieval via visualization6
A novel framework is proposed to help audiences browse and retrieve news video clips without the need of keywords and interesting keyframes and keywords are automatically extracted from News video clips and visually represented according to their interestingness and informativeness measurement.
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- Recurrent Appearance Flow for Occlusion-Free Virtual Try-On5
Experimental results demonstrate that the proposed OF-VTON significantly outperforms existing state-of-the-art methods by mitigating the impact of occlusion problems.
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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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- Deformable Point Cloud Recognition Using Intrinsic Function and Deep Learning3
This paper proposes a novel feature learning approach by using both the model-based intrinsic descriptor and the deep learning technique to represent the isometric deformation by using a set of local intrinsic functions to grasp the invariant properties of the shape.
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- Collective domain adversarial learning for unsupervised domain adaptation2
This work proposes a novel adversarial domain adaptation framework that leverages the collective assumption to model and exploit higher-order interactions among samples and achieves a more robust domain alignment, demonstrating enhanced resilience to noise and domain ambiguity.
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- Mining Large-Scale News Video Database Via Knowledge Visualization2
A novel framework to enable intuitive mining and exploration of large-scale video news databases via knowledge visualization and a statistical video analysis algorithm is proposed to extract the semantics from the video reports.
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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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