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Claim this profileWorks364 from public data
- Improving person re-identification by attribute and identity learning888
An attribute-person recognition (APR) network is proposed, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes, and demonstrates that by learning a more discriminative representation, APR achieves competitive re-IDs performance compared with the state-of-the-art methods.
- Drug–target interaction prediction: databases, web servers and computational models653
In this review, databases and web servers involved in drug-target identification and drug discovery are summarized, and some state-of-the-art computational models for drug- target interactions prediction, including network-based method, machine learning- based method and so on are introduced.
- Unsupervised Person Re-identification636
A progressive unsupervised learning (PUL) method to transfer pretrained deep representations to unseen domains and demonstrates that PUL outputs discriminative features that improve the re-ID accuracy.
- Long non-coding RNAs and complex diseases: from experimental results to computational models567
Some state-of-the-art computational models are introduced, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease- related lnc RNAs for experimental validation and discussed the future directions of developing computational models for lncRNA research.
- Deep Multi-View Enhancement Hashing for Image Retrieval503
This paper proposes a supervised multi-view hash model which can enhance the multi- view information through neural networks, and significantly outperforms the state-of-the-art single-view and multi-View hashing methods.
- CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion423
A novel framework based on convolutional neural networks (CNNs), which transfers the structure of the RGB-based deep neural network to be applicable for depth view and fuses the deep representations of both views automatically to obtain the final saliency map is proposed.
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- WBSMDA: Within and Between Score for MiRNA-Disease Association prediction376
The model of Within and Between Score for MiRNA-Disease Association prediction (WBSMDA) was developed to predict potential miRNAs associated with various complex diseases and would be a useful resource for potential miRNA-disease association identification.
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- Each Part Matters: Local Patterns Facilitate Cross-View Geo-Localization329
It is argued that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization, and introduced a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information in an end-to-end manner.
- Frequency-Aware Feature Fusion for Dense Image Prediction325
Comprehensive visualization and quantitative analysis demonstrate that FreqFusion effectively improves feature consistency and sharpens object boundaries, and extensive experiments across various dense prediction tasks confirm its effectiveness.
- Memory Matching Networks for One-Shot Image Recognition310
Memory Matching Networks (MM-Net) is presented - a novel deep architecture that explores the training procedure, following the philosophy that training and test conditions must match, and improves one-shot accuracy on Omniglot and miniImageNet.
- Unsupervised Person Re-Identification via Softened Similarity Learning307
The iterative training mechanism is followed but clustering is discarded, since it incurs loss from hard quantization, yet its only product, image-level similarity, can be easily replaced by pairwise computation and a softened classification task.
- Gait Recognition in the Wild with Dense 3D Representations and A Benchmark289
This paper proposes a novel framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the human body for gait recognition, named SMPLGait, and provides 3D SMPL models recovered from video frames which can provide dense 3D information of body shape, viewpoint, and dynamics.
- Depth Image Denoising Using Nuclear Norm and Learning Graph Model289
A group-based nuclear norm and learning graph (GNNLG) model, where for each patch, the most similar patches within a searching window are found, which is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.
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- Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity241
Two novel lncRNA functional similarity calculation models (LNCSIM) are developed based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases and some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies.
- Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention238
A novel Multi-feature Fusion and Decomposition framework for age-invariant face recognition, which learns more discriminative and robust features and reduces the intra-class variants and improves the AIFR performance than previous state-of-the-art methods.
- HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction232
The computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) is developed to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNAs associations into a heterogeneous graph.
- Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks191
An asymptotic soft filter pruning (ASFP) method to accelerate the inference procedure of the deep neural networks and shows the effectiveness of the ASFP on image classification benchmarks.
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- Precise No-Reference Image Quality Evaluation Based on Distortion Identification183
This article proposes a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation, and employs the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types.
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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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