Search Results for author:
Found 28 papers, 12 papers with code
UniVG: Towards UNIfied-modal Video Generation
This cannot fully meet the needs of real-world application scenarios, as users are likely to input images and text conditions in a flexible manner, either individually or in combination.
HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models
In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instance as conditions of LDM.
MixTEA: Semi-supervised Entity Alignment with Mixture Teaching
More importantly, in pseudo mapping learning, we propose a bi-directional voting (BDV) strategy that fuses the alignment decisions in different directions to estimate the uncertainty via the joint matching confidence score.
Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging
Unlike traditional neural fields frameworks, LCNF incorporates a local conditional representation that promotes model generalization, learning multiscale information, and efficient processing of large-scale imaging data.
Channel Measurement, Modeling, and Simulation for 6G: A Survey and Tutorial
Then, a survey of the progress of the 6G channel research regarding the above five promising technologies is presented in terms of the latest measurement campaigns, new characteristics, modeling methods, and research prospects.
3GPP-Like THz Channel Modeling for Indoor Office and Urban Microcellular Scenarios
Generally, these results are helpful to understand and model the THz channel and apply the THz communication technique for 6G.
Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network
Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.
Channel Sparsity Variation and Model-Based Analysis on 6, 26, and 132 GHz Measurements
In addition, a new intra-cluster power allocation model based on measurements is proposed to characterize the effects of sparsity in the delay domain of the 3GPP channel model.
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning
In this paper, we propose a novel domain adaptation method, which infers target pseudo-labels through cross-domain label propagation, such that the underlying manifold structure of two domain data can be explored.
Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning
In this work, we propose a stance detection approach that can efficiently adapt to unseen targets, the core of which is to capture target-invariant syntactic expression patterns as transferable knowledge.
Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies
The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide.
Frequency-Angle Two-Dimensional Reflection Coefficient Modeling Based on Terahertz Channel Measurement
In this letter, the reflection coefficient of the THz channel is researched based on extensive measurement campaigns.
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms
We consider the image and video compression on resource limited platforms.
Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields
The representation in DeCAF is learned directly from the measurements of the test sample by using the IDT forward model, without any ground-truth RI maps.
Adaptive 3D descattering with a dynamic synthesis network
In addition, we show that by training the DSN entirely on simulated data, the network can generalize to experiments and achieve robust 3D descattering.
Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples
Using a novel physics model simulator-based learning strategy trained entirely on natural image datasets, we show our network can robustly reconstruct complex 3D biological samples of arbitrary size and structure.
An Adversarial Human Pose Estimation Network Injected with Graph Structure
Because of the invisible human keypoints in images caused by illumination, occlusion and overlap, it is likely to produce unreasonable human pose prediction for most of the current human pose estimation methods.
Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning
Domain adaptation has been a fundamental technology for transferring knowledge from a source domain to a target domain.
High-speed in vitro intensity diffraction tomography
We demonstrate a label-free, scan-free {\it intensity} diffraction tomography technique utilizing annular illumination (aIDT) to rapidly characterize large-volume 3D refractive index distributions in vitro.
Optics Biological Physics
Reliable deep-learning-based phase imaging with uncertainty quantification
We believe our uncertainty learning framework is widely applicable to many DL-based biomedical imaging techniques for assessing the reliability of DL predictions.
Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm
The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm.
Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media
Importantly, instead of characterizing a single input-output relation of a fixed medium, we train our CNN to learn statistical information contained in several scattering media of the same class.
Deep learning approach to Fourier ptychographic microscopy
Specifically, we show that it is possible to reconstruct high-SBP dynamic cell videos by a CNN trained only on the first FPM dataset captured at the beginning of a time-series experiment.
High-throughput intensity diffraction tomography with a computational microscope
We further investigate the limitation of our technique when imaging strongly scattering samples.
Image and Video Processing Biological Physics Optics
PCANet-II: When PCANet Meets the Second Order Pooling
Moreover, it is easy to combine other discriminative and robust cues by using the second order pooling.
Compressive Holographic Video
Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image.
Structured illumination microscopy with unknown patterns and a statistical prior
Structured illumination microscopy (SIM) improves resolution by down-modulating high-frequency information of an object to fit within the passband of the optical system.
Experimental robustness of Fourier Ptychography phase retrieval algorithms
Both noise (e. g. Poisson noise) and model mis-match errors are shown to scale with intensity.
