Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks17 from public data
- Learning Event-Driven Video Deblurring and Interpolation163
An effective event-driven video deblurring and interpolation algorithm based on deep convolutional neural networks (CNNs) that achieves superior performance against state-of-the-art methods on both synthetic and real datasets.
- DVS-Voltmeter: Stochastic Process-Based Event Simulator for Dynamic Vision Sensors79
An event simulator, dubbed DVS-Voltmeter, is proposed to enable high-performance deep networks for DVS applications and indicates that neural networks trained with DVS-Voltmeter generalize favorably on real events against state-of-the-art simulators.
- 48
- 36
- 34
- 30
- Learning to Deblur Face Images via Sketch Synthesis26
An effective face deblurring algorithm based on deep convolutional neural networks (CNNs) that is able to deblur face images with favorable performance against state-of-the-art methods.
- 15
- Compressed Event Sensing (CES) Volumes for Event Cameras9
CES volumes preserve the high temporal resolution of event streams by leveraging the sparsity property of events and the principles of compressed sensing theory, and effectively capture the frequency characteristics of events in low-dimensional representations.
- Dual-head Genre-instance Transformer Network for Arbitrary Style Transfer5
This work proposes a Dual-head Genre-instance Transformer (DGiT) framework to simultaneously capture the genre and instance features for arbitrary style transfer and is the first work to integrate the genre features and instance features to generate a high-quality stylized image.
- 3
- Cross-spectral stereo matching for facial disparity estimation in the dark3
A neural network composed of a multi-spectral transfer network (MSTN) and a disparity estimation network (DEN) that performs favorably against state-of-the-art algorithms on both synthetic and real data is developed.
- 2
- 1
- Learning Instance Motion Segmentation With Geometric Embedding1
A joint learning method which fuses semantic features and motion clues using CNNs with deformable convolution and a motion embedding module, to address multi-object motion segmentation problem.
- –
- Scale Estimation and Refinement in Monocular Visual-Inertial SLAM System–
This paper presents an approach to estimate scale, gravity and accelerometer bias together, and regard the estimated gravity as an indication for estimation convergence, and proposes a methodology that is able to use weight derived from the robust norm for outliers handling, so that the estimated scale can be refined.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. 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. You will be asked to sign in.