Search Results for author:
Found 56 papers, 10 papers with code
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory
Graph matching, also known as network alignment, aims at recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs.
On the best approximation by finite Gaussian mixtures
We consider the problem of approximating a general Gaussian location mixture by finite mixtures.
An Accurate and Real-time Relative Pose Estimation from Triple Point-line Images by Decoupling Rotation and Translation
First, a high-precision rotation estimation method based on normal vector coplanarity constraints that consider the uncertainty of observations is proposed, which can be solved by Levenberg-Marquardt (LM) algorithm efficiently.
NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer
Visual Place Recognition (VPR) is crucial in computer vision, aiming to retrieve database images similar to a query image from an extensive collection of known images.
ViSTec: Video Modeling for Sports Technique Recognition and Tactical Analysis
The immense popularity of racket sports has fueled substantial demand in tactical analysis with broadcast videos.
Optimal score estimation via empirical Bayes smoothing
We study the problem of estimating the score function of an unknown probability distribution $\rho^*$ from $n$ independent and identically distributed observations in $d$ dimensions.
MuGI: Enhancing Information Retrieval through Multi-Text Generation Integration with Large Language Models
Large Language Models (LLMs) have emerged as a pivotal force in language technology.
Depth Insight -- Contribution of Different Features to Indoor Single-image Depth Estimation
To this end, in this work, we quantify the relative contributions of the known cues of depth in a monocular depth estimation setting using an indoor scene data set.
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation
Second, we develop a transition-aware embedding distillation module that distills global item-to-item transition patterns into item embeddings, which enables the model to memorize and leverage transitional signals and serves as a calibrator for collaborative signals.
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
To enable LLMs to tackle the task in a zero-shot manner, we introduce MoqaGPT, a straightforward and flexible framework.
MatSpectNet: Material Segmentation Network with Domain-Aware and Physically-Constrained Hyperspectral Reconstruction
The network leverages the principles of colour perception in modern cameras to constrain the reconstructed hyperspectral images and employs the domain adaptation method to generalise the hyperspectral reconstruction capability from a spectral recovery dataset to material segmentation datasets.
ConvGQR: Generative Query Reformulation for Conversational Search
In this paper, we propose ConvGQR, a new framework to reformulate conversational queries based on generative pre-trained language models (PLMs), one for query rewriting and another for generating potential answers.
All-condition pulse detection using a magnetic sensor
A plethora of wearable devices have been developed or commercialized for continuous non-invasive monitoring of physiological signals that are crucial for preventive care and management of chronic conditions.
NPR: Nocturnal Place Recognition in Streets
Then we used this model to process existing large-scale VPR datasets to generate the VPR-Night datasets and demonstrated how to combine them with two popular VPR pipelines.
Random graph matching at Otter's threshold via counting chandeliers
We propose an efficient algorithm for graph matching based on similarity scores constructed from counting a certain family of weighted trees rooted at each vertex.
Random Graph Matching in Geometric Models: the Case of Complete Graphs
This paper studies the problem of matching two complete graphs with edge weights correlated through latent geometries, extending a recent line of research on random graph matching with independent edge weights to geometric models.
Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching
In this paper, we present a novel lightweight object-level mapping and localization method with high accuracy and robustness.
Scale-Net: Learning to Reduce Scale Differences for Large-Scale Invariant Image Matching
To solve this problem, firstly, we propose a scale-difference-aware image matching method (SDAIM) that reduces image scale differences before local feature extraction, via resizing both images of an image pair according to an estimated scale ratio.
Testing network correlation efficiently via counting trees
We propose a new procedure for testing whether two networks are edge-correlated through some latent vertex correspondence.
Sharp regret bounds for empirical Bayes and compound decision problems
We show that for the Poisson model with compactly supported and subexponential priors, the optimal regret scales as $\Theta((\frac{\log n}{\log\log n})^2)$ and $\Theta(\log^3 n)$, respectively, both attained by the original estimator of Robbins.
The planted matching problem: Sharp threshold and infinite-order phase transition
Conversely, if $\sqrt{d} B(\mathcal{P},\mathcal{Q}) \ge 1+\epsilon$ for an arbitrarily small constant $\epsilon>0$, the reconstruction error for any estimator is shown to be bounded away from $0$ under both the sparse and dense model, resolving the conjecture in [Moharrami et al. 2019, Semerjian et al. 2020].
Settling the Sharp Reconstruction Thresholds of Random Graph Matching
This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs.
EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos
The popularity of racket sports (e. g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance.
Semi-Global Shape-aware Network
In this paper, we propose a Semi-Global Shape-aware Network (SGSNet) considering both feature similarity and proximity for preserving object shapes when modeling long-range dependencies.
Leveraging Local and Global Descriptors in Parallel to Search Correspondences for Visual Localization
Each of the 2D image points is also called a query local feature when performing the 2D-3D point correspondences.
Learning Mixtures of Permutations: Groups of Pairwise Comparisons and Combinatorial Method of Moments
In applications such as rank aggregation, mixture models for permutations are frequently used when the population exhibits heterogeneity.
Testing correlation of unlabeled random graphs
We study the problem of detecting the edge correlation between two random graphs with $n$ unlabeled nodes.
Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models
Notably, any such Gaussian mixture is statistically indistinguishable from a finite one with $O(\log n)$ components (and this is tight for certain mixtures).
VisImages: A Fine-Grained Expert-Annotated Visualization Dataset
Images in visualization publications contain rich information, e. g., novel visualization designs and implicit design patterns of visualizations.
Extrapolating the profile of a finite population
Nevertheless, we show that in the sublinear regime of $m =\omega(k/\log k)$, it is possible to consistently estimate in total variation the \emph{profile} of the population, defined as the empirical distribution of the sizes of each type, which determines many symmetric properties of the population.
Optimal estimation of high-dimensional location Gaussian mixtures
This paper studies the optimal rate of estimation in a finite Gaussian location mixture model in high dimensions without separation conditions.
Consistent recovery threshold of hidden nearest neighbor graphs
Motivated by applications such as discovering strong ties in social networks and assembling genome subsequences in biology, we study the problem of recovering a hidden $2k$-nearest neighbor (NN) graph in an $n$-vertex complete graph, whose edge weights are independent and distributed according to $P_n$ for edges in the hidden $2k$-NN graph and $Q_n$ otherwise.
Randomly initialized EM algorithm for two-component Gaussian mixture achieves near optimality in $O(\sqrt{n})$ iterations
We analyze the classical EM algorithm for parameter estimation in the symmetric two-component Gaussian mixtures in $d$ dimensions.
Efficient Circle-Based Camera Pose Tracking Free of PnP
Then, PnP and RANSAC are used to compute the camera pose.
Spectral Graph Matching and Regularized Quadratic Relaxations II: Erdős-Rényi Graphs and Universality
We analyze a new spectral graph matching algorithm, GRAph Matching by Pairwise eigen-Alignments (GRAMPA), for recovering the latent vertex correspondence between two unlabeled, edge-correlated weighted graphs.
Spectral Graph Matching and Regularized Quadratic Relaxations I: The Gaussian Model
Departing from prior spectral approaches that only compare top eigenvectors, or eigenvectors of the same order, GRAMPA first constructs a similarity matrix as a weighted sum of outer products between all pairs of eigenvectors of the two graphs, with weights given by a Cauchy kernel applied to the separation of the corresponding eigenvalues, then outputs a matching by a simple rounding procedure.
Automatically Extract the Semi-transparent Motion-blurred Hand from a Single Image
The proposed method separates the extraction task into two subtasks: alpha matte prediction and foreground prediction.
Data Amplification: A Unified and Competitive Approach to Property Estimation
We design the first unified, linear-time, competitive, property estimator that for a wide class of properties and for all underlying distributions uses just $2n$ samples to achieve the performance attained by the empirical estimator with $n\sqrt{\log n}$ samples.
Efficient random graph matching via degree profiles
This work develops an $\tilde{O}(n d^2+n^2)$-time algorithm which perfectly recovers the true vertex correspondence with high probability, provided that the average degree is at least $d = \Omega(\log^2 n)$ and the two graphs differ by at most $\delta = O( \log^{-2}(n) )$ fraction of edges.
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.
Heteroskedastic PCA: Algorithm, Optimality, and Applications
A general framework for principal component analysis (PCA) in the presence of heteroskedastic noise is introduced.
Hidden Hamiltonian Cycle Recovery via Linear Programming
Represented as bicolored multi-graphs, these extreme points are further decomposed into simpler "blossom-type" structures for the large deviation analysis and counting arguments.
Entropy Rate Estimation for Markov Chains with Large State Space
For estimating the Shannon entropy of a distribution on $S$ elements with independent samples, [Paninski2004] showed that the sample complexity is sublinear in $S$, and [Valiant--Valiant2011] showed that consistent estimation of Shannon entropy is possible if and only if the sample size $n$ far exceeds $\frac{S}{\log S}$.
Counting Motifs with Graph Sampling
Applied researchers often construct a network from a random sample of nodes in order to infer properties of the parent network.
Estimating the Number of Connected Components in a Graph via Subgraph Sampling
Learning properties of large graphs from samples has been an important problem in statistical network analysis since the early work of Goodman \cite{Goodman1949} and Frank \cite{Frank1978}.
Sample complexity of population recovery
For noisy population recovery, the sharp sample complexity turns out to be more sensitive to dimension and scales as $\exp(\Theta(d^{1/3} \log^{2/3}(1/\delta)))$ except for the trivial cases of $\epsilon=0, 1/2$ or $1$.
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices.
Image Based Camera Localization: an Overview
In this paper, an overview of image based camera localization is presented.
Semidefinite Programs for Exact Recovery of a Hidden Community
We study a semidefinite programming (SDP) relaxation of the maximum likelihood estimation for exactly recovering a hidden community of cardinality $K$ from an $n \times n$ symmetric data matrix $A$, where for distinct indices $i, j$, $A_{ij} \sim P$ if $i, j$ are both in the community and $A_{ij} \sim Q$ otherwise, for two known probability distributions $P$ and $Q$.
Estimating the number of unseen species: A bird in the hand is worth $\log n $ in the bush
We derive a class of estimators that $\textit{provably}$ predict $U$ not just for constant $t>1$, but all the way up to $t$ proportional to $\log n$.
Submatrix localization via message passing
The principal submatrix localization problem deals with recovering a $K\times K$ principal submatrix of elevated mean $\mu$ in a large $n\times n$ symmetric matrix subject to additive standard Gaussian noise.
Recovering a Hidden Community Beyond the Kesten-Stigum Threshold in $O(|E| \log^*|V|)$ Time
We show that a belief propagation algorithm achieves weak recovery if $\lambda>1/e$, beyond the Kesten-Stigum threshold by a factor of $1/e.$ The belief propagation algorithm only needs to run for $\log^\ast n+O(1) $ iterations, with the total time complexity $O(|E| \log^*n)$, where $\log^*n$ is the iterated logarithm of $n.$ Conversely, if $\lambda \leq 1/e$, no local algorithm can asymptotically outperform trivial random guessing.
Information Limits for Recovering a Hidden Community
We study the problem of recovering a hidden community of cardinality $K$ from an $n \times n$ symmetric data matrix $A$, where for distinct indices $i, j$, $A_{ij} \sim P$ if $i, j$ both belong to the community and $A_{ij} \sim Q$ otherwise, for two known probability distributions $P$ and $Q$ depending on $n$.
Achieving Exact Cluster Recovery Threshold via Semidefinite Programming: Extensions
Extending the proof techniques, in this paper it is shown that SDP relaxations also achieve the sharp recovery threshold in the following cases: (1) Binary stochastic block model with two clusters of sizes proportional to network size but not necessarily equal; (2) Stochastic block model with a fixed number of equal-sized clusters; (3) Binary censored block model with the background graph being Erd\H{o}s-R\'enyi.
Achieving Exact Cluster Recovery Threshold via Semidefinite Programming
The binary symmetric stochastic block model deals with a random graph of $n$ vertices partitioned into two equal-sized clusters, such that each pair of vertices is connected independently with probability $p$ within clusters and $q$ across clusters.
Computational Lower Bounds for Community Detection on Random Graphs
This paper studies the problem of detecting the presence of a small dense community planted in a large Erd\H{o}s-R\'enyi random graph $\mathcal{G}(N, q)$, where the edge probability within the community exceeds $q$ by a constant factor.
