Search Results for author:
Found 27 papers, 9 papers with code
3D GAN Inversion with Facial Symmetry Prior
It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, referred as 3D GAN inversion.
A Benchmark of Video-Based Clothes-Changing Person Re-Identification
In this paper, we focus on the relatively new yet practical problem of clothes-changing video-based person re-identification (CCVReID), which is less studied.
Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars
We propose a novel 3D GAN framework for unsupervised learning of generative, high-quality and 3D-consistent facial avatars from unstructured 2D images.
CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion
As such, we propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and LiDAR data and significantly reduces tracking failures caused by occlusion and false detection.
FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing
Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method enables face reconstruction across different persons with few-shot inputs.
Formal guarantees for heuristic optimization algorithms used in machine learning
We show that their last iterate has optimal convergence for unconstrained convex stochastic optimization problems.
Neural Parameterization for Dynamic Human Head Editing
Implicit radiance functions emerged as a powerful scene representation for reconstructing and rendering photo-realistic views of a 3D scene.
UV Volumes for Real-time Rendering of Editable Free-view Human Performance
Neural volume rendering enables photo-realistic renderings of a human performer in free-view, a critical task in immersive VR/AR applications.
Hallucinated Neural Radiance Fields in the Wild
This paper studies the problem of hallucinated NeRF: i. e., recovering a realistic NeRF at a different time of day from a group of tourism images.
FENeRF: Face Editing in Neural Radiance Fields
2D GANs can generate high fidelity portraits but with low view consistency.
Deblur-NeRF: Neural Radiance Fields from Blurry Images
We demonstrate that our method can be used on both camera motion blur and defocus blur: the two most common types of blur in real scenes.
Let's See Clearly: Contaminant Artifact Removal for Moving Cameras
This new dataset and our novel framework lead to our method that is able to address different contaminants and outperforms competitive restoration approaches both qualitatively and quantitatively.
Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.
High Energy Physics - Experiment
On the Last Iterate Convergence of Momentum Methods
In this paper, we focus on the convergence rate of the last iterate of SGDM.
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
Deep Sketch-guided Cartoon Video Inbetweening
The key idea of the proposed approach is to estimate the dense cross-domain correspondence between the sketch and cartoon video frames, and employ a blending module with occlusion estimation to synthesize the middle frame guided by the sketch.
A High Probability Analysis of Adaptive SGD with Momentum
We use it to prove for the first time the convergence of the gradients to zero in high probability in the smooth nonconvex setting for Delayed AdaGrad with momentum.
A Second look at Exponential and Cosine Step Sizes: Simplicity, Adaptivity, and Performance
Moreover, we show the surprising property that these two strategies are \emph{adaptive} to the noise level in the stochastic gradients of PL functions.
Document Rectification and Illumination Correction using a Patch-based CNN
We propose a novel learning method to rectify document images with various distortion types from a single input image.
Blind Geometric Distortion Correction on Images Through Deep Learning
We propose the first general framework to automatically correct different types of geometric distortion in a single input image.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Natural Language Processing for EHR-Based Computational Phenotyping
This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping.
Using Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer
Our model can accurately and efficiently identify distant recurrences in breast cancer by combining features extracted from unstructured clinical narratives and structured clinical data.
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes
In this paper, we start closing this gap: we theoretically analyze in the convex and non-convex settings a generalized version of the AdaGrad stepsizes.
Quantum Laplacian Eigenmap
While classical Laplacian eigenmap algorithm requires polynomial time to solve the eigenvector problem, our algorithm is able to exponentially speed up nonlinear dimensionality reduction.
Machine Learned Resume-Job Matching Solution
In our solution, rather than using manual rules, machine learned methods to automatically detect the semantic similarity of positions are proposed.
An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding
In this article, how word embeddings can be used as features in Chinese sentiment classification is presented.
