Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks264 from public data
- Hetero-Center loss for cross-modality person Re-identification222
A novel loss function, called Hetero-Center loss (HC loss) is proposed to reduce the intra-class cross-modality variations and a simple and high-performance network architecture to learn local feature representations for cross- modality person re-identification is proposed, which can be a baseline for future research.
- Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning216
This paper develops a novel medical image fusion, denoising, and enhancement method based on low-rank sparse component decomposition and dictionary learning that consistently outperforms existing state-of-the-art methods in terms of both visual and quantitative evaluations.
- Person Re-Identification by Regularized Smoothing KISS Metric Learning174
This paper presents regularized smoothing KISS metric learning (RS-KISS) by seamlessly integrating smoothing and regularization techniques for robustly estimating covariance matrices and introduces incremental learning to RS-K ISS.
- 137
- 137
- 124
- Canonical correlation analysis networks for two-view image recognition123
A canonical correlation analysis network (CCANet) is presented to address image classification, in which images are represented by two-view features, and the experimental results demonstrate that the CCANet algorithm is more effective than PCANet, RandNet-1 andRandNet-2.
- Upper limb motion tracking with the integration of IMU and Kinect119
This paper investigated how to fuse data from internal sensors of IMU, and fuse IMU data with Kinect in order to provide robust hand position information compensated for the limitations of those sensors, and found the proposed IMU and Kinect fusion method can provide drift-free and smooth results.
- Semantic preserving distance metric learning and applications115
A Semantic Preserving Distance Metric Learning (SP-DML) algorithm is developed to explore the complementary characteristics of the visual features and pairwise constraints in a unified feature space to integrate semantic contents in distance metric learning.
- 112
- 111
- 109
- 109
- Hessian Regularized Support Vector Machines for Mobile Image Annotation on the Cloud108
A scheme for image annotation on the cloud is presented, which transmits mobile images compressed by Hamming compressed sensing to the cloud and conducts semantic annotation through a novel Hessian regularized support vector machine on thecloud.
- 106
- Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld105
This paper distill LLM's reflection outcomes in a text world's tasks to finetune the VLM on the same tasks of the visual world, resulting in an Embodied Multi-Modal Agent (EMMA) quickly adapting to the visual world dy-namics.
- 104
- Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters104
It is proposed that font recognition on a single Chinese character is a sequence classification problem, which can be effectively solved by recurrent neural networks and integrated a principal component convolution layer with the 2-D long short-term memory (2DLSTM) and developed principal component 2 DLSTM (PC-2DL STM) algorithm.
- 103
- Multiview dimension reduction via Hessian multiset canonical correlations100
HesMCC is presented, which takes the advantage of Hessian and provides superior extrapolating capability and finally leverage the performance of TCCA, KMUDA, MCCA and LapMCC for multiview dimension reduction.
- 99
- Skeleton embedded motion body partition for human action recognition using depth sequences94
An effective approach, which is insensitive to various temporal duration and adequate for complex background, for human action recognition using depth sequences, is proposed by embedding the skeleton information into depth maps by taking account of the geometrical structure of human body and contribute to the recognition task in complex background.
- 93
- 93
- Deep features for person re-identification on metric learning92
This work summarizes the different types of features and metric learning approaches from a label attributes perspective, and conducts comprehensive experiments on metric learning methods with two datasets, showing that the relations of loss function with deep feature space and metriclearning.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.