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- Stan Ziqing LiSuggested from co-authorship
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Works140 from public data
- GiT: Graph Interactive Transformer for Vehicle Re-Identification178
A graph interactive transformer (GiT) is proposed, which is a newly-designed local correction graph, which learns discriminative local features within a patch by exploring nodes’ relationships and is superior to state-of-the-art vehicle re-identification approaches.
- Vehicle Re-Identification Using Quadruple Directional Deep Learning Features158
The proposed QD-DLF approach outperforms multiple state-of-the-art vehicle re-identification methods and is applicable to both VeRi and VehicleID databases.
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- Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-Identification126
Extensive experiments on three large scale vehicle databases demonstrate that the proposed HPGN is superior to state-of-the-art vehicle re-identification approaches in terms of accuracy, parameter cost, and computation cost.
- Multiple improved residual networks for medical image super-resolution116
The proposed multiple improved residual network (MIRN) super-resolution reconstruction method can better reconstruct the details and textures of images and avoid the over-smoothing of medical images after iteration, and the performance of the proposed algorithm is revealed to be better than that of existing state-of-the-art methods.
- Super-resolution reconstruction of knee magnetic resonance imaging based on deep learning113
The experimental results show that the EMISR framework has improved performance and greatly reduces the number of parameters and training time and provides a more powerful medical analysis in knee osteoarthritis examinations.
- Multi-label convolutional neural network based pedestrian attribute classification107
The proposed multi-label convolutional neural network (MLCNN) can simultaneously predict multiple pedestrian attributes and significantly outperforms the SVM based method on the PETA database.
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- A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images86
The proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.
- Deep Hybrid Similarity Learning for Person Re-Identification84
A deep hybrid similarity learning (DHSL) method based on a convolution neural network that is able to reasonably assign complexities of feature learning and metric learning in a CNN, so that the performance of person Re-ID is improved.
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- A multi-order derivative feature-based quality assessment model for light field image60
The proposed MDFM is more consistent with the perception of the HVS on the evaluation of LFI than the classical and state-of-the-art IQA methods.
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- HOG-assisted deep feature learning for pedestrian gender recognition51
Extensive experiments have shown that the proposed HDFL method is able to effectively recognize the pedestrian gender, and consistently outperforms the state-of-the-art methods.
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- Joint Feature and Similarity Deep Learning for Vehicle Re-identification38
The proposed JFSDL method applies a siamese deep network to extract deep learning features for an input vehicle image pair simultaneously and is superior to multiple state-of-the-art vehicle re-identification methods on both the VehicleID and VeRi data sets.
- A multi-scale contrast-based image quality assessment model for multi-exposure image fusion37
Simulation results have clearly shown that the proposed multi-scale contrast-based model (MCM) model is more consistent with the perception of the HVS on the evaluation of MEF images than multiple state-of-the-art IQA 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 2026-10-16. 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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