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- Chun ChenSuggested from co-authorship
- Xuelong LiSuggested from co-authorship
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Works235 from public data
- 297
- Fortune Teller: Predicting Your Career Path258
This work scientifically and systematically study the feasibility of career path prediction from social network data and seamlessly fuse information from multiple social networks to comprehensively describe a user and characterize progressive properties of his or her career path.
- Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation240
Experimental results show that the proposed approach outperforms state-of-the-art weakly-supervised image segmentation methods, on five popular segmentation data sets and performs competitively to the fully- supervised segmentation models.
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- Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation172
A new photo aesthetics evaluation framework is proposed, focusing on learning the image descriptors that characterize local and global structural aesthetics from multiple visual channels, which significantly outperforms state-of-the-art algorithms in photo aesthetics prediction.
- GMove162
This work proposes GMove, a group-level mobility modeling method using GeoSM data that alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group- level movement regularity.
- 159
- The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers150
This survey focuses on algebraic word problems, summarize their extracted features and proposed techniques to bridge the semantic gap, and compare their performance in the publicly accessible datasets.
- A Probabilistic Associative Model for Segmenting Weakly Supervised Images146
This paper proposes a new weakly supervised image segmentation model, focusing on learning the semantic associations between superpixel sets (graphlets in this paper), and presents a hierarchical Bayesian network to capture the semantic association between postembedding graphlets.
- 145
- A Fine-Grained Image Categorization System by Cellet-Encoded Spatial Pyramid Modeling144
A new fine-grained image categorization system that improves spatial pyramid matching is developed that outperforms the state of the art and can be conducted with a trained linear support vector machine.
- Exploring global diverse attention via pairwise temporal relation for video summarization135
An efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention called SUM-GDA, which adapts attention mechanism in a global perspective to consider pairwise temporal relations of video frames, is proposed.
- 127
- Beyond Doctors121
An adaptive multimodal multi-task learning model is proposed to co-regularize the modality agreement, temporal progression and discriminative capabilities of different modalities of Alzheimer's disease and is theoretically show that it is a linear system.
- 119
- Multiple Social Network Learning and Its Application in Volunteerism Tendency Prediction103
A novel model for data missing completion is proposed that is applicable to many other domains, such as demographic inference and interest prediction, and a robust multiple social network learning model is developed and applied to the application of volunteerism tendency prediction.
- Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s Disease99
A novel and unified scheme to coregularize the prior knowledge of source consistency and temporal smoothness of chronic disease progression is proposed and theoretically proves that the proposed model is a linear model.
- Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest)98
A prognostic model to predict a 30-day mortality risk in elderly patients with sepsis based on machine learning (RSF algorithm), and it proved superior to the traditional scoring systems.
- 98
- Actively Learning Human Gaze Shifting Paths for Semantics-Aware Photo Cropping96
Semantics-aware photo cropping is proposed, which crops a photo by simulating the process of humans sequentially perceiving semantically important regions of a photo, and the cropped photos produced by the approach outperform its competitors in both qualitative and quantitative comparisons.
- A mix-pooling CNN architecture with FCRF for brain tumor segmentation94
A two-pathway model with average and max pooling layers in different paths is designed and combined with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results.
- Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection89
A nomogram prediction model was constructed to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection and revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets.
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 2026-10-12. 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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