Search Results for author:
Found 22 papers, 8 papers with code
Gene Regulatory Network Inference with Covariance Dynamics
Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data.
MetaSD: A Unified Framework for Scalable Downscaling of Meteorological Variables in Diverse Situations
This framework aims to facilitate the downscaling of diverse meteorological variables derived from various numerical models and spatiotemporal scales.
D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes
To address this issue, we propose D-Aug, a LiDAR data augmentation method tailored for augmenting dynamic scenes.
SemanticHuman-HD: High-Resolution Semantic Disentangled 3D Human Generation
Notably, SemanticHuman-HD is also the first method to achieve 3D-aware image synthesis at $1024^2$ resolution, benefiting from our proposed 3D-aware super-resolution module.
TaylorGrid: Towards Fast and High-Quality Implicit Field Learning via Direct Taylor-based Grid Optimization
In this paper, we aim for both fast and high-quality implicit field learning, and propose TaylorGrid, a novel implicit field representation which can be efficiently computed via direct Taylor expansion optimization on 2D or 3D grids.
Discriminative Consensus Mining with A Thousand Groups for More Accurate Co-Salient Object Detection
Co-Salient Object Detection (CoSOD) is a rapidly growing task, extended from Salient Object Detection (SOD) and Common Object Segmentation (Co-Segmentation).
3D-SSGAN: Lifting 2D Semantics for 3D-Aware Compositional Portrait Synthesis
Conversely, some GAN-based 2D portrait synthesis methods can achieve clear disentanglement of facial regions, but they cannot preserve view consistency due to a lack of 3D modeling abilities.
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
It comprises two essential components: the localization module (LM) and the reconstruction module (RM) with our proposed bilateral reference (BiRef).
Ranked #1 on
Camouflaged Object Segmentation
on COD
Camouflaged Object Segmentation
Dichotomous Image Segmentation
+3
Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation
As growing usage of social media websites in the recent decades, the amount of news articles spreading online rapidly, resulting in an unprecedented scale of potentially fraudulent information.
Memory-aided Contrastive Consensus Learning for Co-salient Object Detection
To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories.
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes.
Ranked #1 on
Co-Salient Object Detection
on CoCA
Facial-Sketch Synthesis: A New Challenge
Besides, we elaborate comprehensive experiments on the existing 19 cutting-edge models.
MovieNet-PS: A Large-Scale Person Search Dataset in the Wild
Person search aims to jointly localize and identify a query person from natural, uncropped images, which has been actively studied over the past few years.
Ranked #3 on
Person Search
on CUHK-SYSU
Trimmed Constrained Mixed Effects Models: Formulations and Algorithms
We consider ME models where the random effects component is linear.
A unified sparse optimization framework to learn parsimonious physics-informed models from data
This flexible approach can be tailored to the unique challenges associated with a wide range of applications and data sets, providing a powerful ML-based framework for learning governing models for physical systems from data.
Object Detection with Deep Learning: A Review
Traditional object detection methods are built on handcrafted features and shallow trainable architectures.
A Unified Framework for Sparse Relaxed Regularized Regression: SR3
We demonstrate the advantages of SR3 (computational efficiency, higher accuracy, faster convergence rates, greater flexibility) across a range of regularized regression problems with synthetic and real data, including applications in compressed sensing, LASSO, matrix completion, TV regularization, and group sparsity.
Computer Assisted Localization of a Heart Arrhythmia
We consider the problem of locating a point-source heart arrhythmia using data from a standard diagnostic procedure, where a reference catheter is placed in the heart, and arrival times from a second diagnostic catheter are recorded as the diagnostic catheter moves around within the heart.
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM
Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems.
Sparse Principal Component Analysis via Variable Projection
Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying localized spatial structures in the data and disambiguating between distinct time scales.
Learning Robust Representations for Computer Vision
Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional linear and non-linear subspaces.
Estimating Shape Parameters of Piecewise Linear-Quadratic Problems
The normalization constant inherent in this requirement helps to inform the optimization over shape parameters, giving a joint optimization problem over these as well as primary parameters of interest.
