1 code implementation • 1 Apr 2024 • Tianyu Huang, Liangzu Peng, René Vidal, Yun-hui Liu

Given an input set of $3$D point pairs, the goal of outlier-robust $3$D registration is to compute some rotation and translation that align as many point pairs as possible.

1 code implementation • 31 Mar 2024 • Yashar Deldjoo, Zhankui He, Julian McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano

Traditional recommender systems (RS) have used user-item rating histories as their primary data source, with collaborative filtering being one of the principal methods.

1 code implementation • 11 Mar 2024 • Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou

The Stochastic Extragradient (SEG) method is one of the most popular algorithms for solving finite-sum min-max optimization and variational inequality problems (VIPs) appearing in various machine learning tasks.

no code implementations • 29 Nov 2023 • Jinqi Luo, Kwan Ho Ryan Chan, Dimitris Dimos, René Vidal

To address this question, we propose Knowledge Pursuit Prompting (KPP), a zero-shot framework that iteratively incorporates external knowledge to help generators produce reliable visual content.

no code implementations • 24 Jul 2023 • Hancheng Min, Enrique Mallada, René Vidal

Our analysis shows that, during the early phase of training, neurons in the first layer try to align with either the positive data or the negative data, depending on its corresponding weight on the second layer.

1 code implementation • 8 Jun 2023 • Tianzhe Chu, Shengbang Tong, Tianjiao Ding, Xili Dai, Benjamin David Haeffele, René Vidal, Yi Ma

In this paper, we propose a novel image clustering pipeline that leverages the powerful feature representation of large pre-trained models such as CLIP and cluster images effectively and efficiently at scale.

no code implementations • 7 Jun 2023 • Darshan Thaker, Paris Giampouras, René Vidal

In this paper, we build on prior work and propose a novel framework for reverse engineering of deceptions which supposes that the clean data lies in the range of a GAN.

1 code implementation • 29 Apr 2023 • Liangzu Peng, Paris V. Giampouras, René Vidal

We show that ICL unifies multiple well-established continual learning methods and gives new theoretical insights into the strengths and weaknesses of these methods.

no code implementations • 11 Apr 2023 • Kyle Poe, Enrique Mallada, René Vidal

In this work, we provide (1) the first characterization of necessary and sufficient conditions for the existence and uniqueness of sparse inputs to an LDS, (2) the first necessary and sufficient conditions for a linear program to recover both an unknown initial state and a sparse input, and (3) simple, interpretable recovery conditions in terms of the LDS parameters.

1 code implementation • 6 Feb 2023 • Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin D. Haeffele, Donald Geman, René Vidal

We then demonstrate that the IP strategy is the optimal solution to this problem.

1 code implementation • CVPR 2023 • Liangzu Peng, Christian Kümmerle, René Vidal

Outlier-robust estimation involves estimating some parameters (e. g., 3D rotations) from data samples in the presence of outliers, and is typically formulated as a non-convex and non-smooth problem.

no code implementations • 1 Dec 2022 • Ambar Pal, Arnau Ramisa, Amit Kumar K C, René Vidal

However, obtaining a large amount of training annotations specific to a particular task, i. e., fine-grained annotations, is costly in practice.

no code implementations • 7 Nov 2022 • Yutao Tang, Benjamín Béjar, Joey K. -Y. Essoe, Joseph F. McGuire, René Vidal

Behavioral therapy is the first-line treatment for patients with TS, and it helps patients raise awareness about tic occurrence as well as develop tic inhibition strategies.

no code implementations • 18 Jul 2022 • Liangzu Peng, Mahyar Fazlyab, René Vidal

To induce robustness against outliers for rotation search, prior work considers truncated least-squares (TLS), which is a non-convex optimization problem, and its semidefinite relaxation (SDR) as a tractable alternative.

1 code implementation • 16 Jun 2022 • Kaleab A. Kinfu, René Vidal

Our first contribution is to show that generating optimal attacks for video requires carefully tuning the attack parameters, especially the step size.

1 code implementation • CVPR 2022 • Liangzu Peng, Manolis C. Tsakiris, René Vidal

We first propose a solver, $\texttt{ARCS}$, that i) assumes noiseless point sets in general position, ii) requires only $2$ inliers, iii) uses $O(m\log m)$ time and $O(m)$ space, and iv) can successfully solve the problem even with, e. g., $m, n\approx 10^6$ in about $0. 1$ seconds.

no code implementations • 9 Mar 2022 • Darshan Thaker, Paris Giampouras, René Vidal

We pose this problem as a block-sparse recovery problem, where both the signal and the attack are assumed to lie in a union of subspaces that includes one subspace per class and one subspace per attack type.

no code implementations • 22 Jan 2022 • Paris V. Giampouras, Benjamin D. Haeffele, René Vidal

Robust subspace recovery (RSR) is a fundamental problem in robust representation learning.

no code implementations • CVPR 2022 • Effrosyni Mavroudi, René Vidal

Given weak supervision from image- or video-caption pairs, we address the problem of grounding (localizing) each object word of a ground-truth or generated sentence describing a visual input.

1 code implementation • CVPR 2021 • Shangzhi Zhang, Chong You, René Vidal, Chun-Guang Li

We show that our SENet can not only learn the self-expressive coefficients with desired properties on the training data, but also handle out-of-sample data.

1 code implementation • 30 Nov 2020 • Derek Lim, René Vidal, Benjamin D. Haeffele

Many state-of-the-art subspace clustering methods follow a two-step process by first constructing an affinity matrix between data points and then applying spectral clustering to this affinity.

Ranked #1 on Image Clustering on UMist

no code implementations • ICLR 2021 • Benjamin D. Haeffele, Chong You, René Vidal

To extend this approach to data supported on a union of non-linear manifolds, numerous studies have proposed learning an embedding of the original data using a neural network which is regularized by a self-expressive loss function on the data in the embedded space to encourage a union of linear subspaces prior on the data in the embedded space.

no code implementations • NeurIPS 2020 • Ambar Pal, René Vidal

Research in adversarial learning follows a cat and mouse game between attackers and defenders where attacks are proposed, they are mitigated by new defenses, and subsequently new attacks are proposed that break earlier defenses, and so on.

no code implementations • 15 Apr 2020 • Guilherme França, Michael. I. Jordan, René Vidal

More specifically, we show that a generalization of symplectic integrators to nonconservative and in particular dissipative Hamiltonian systems is able to preserve rates of convergence up to a controlled error.

no code implementations • 20 Jan 2020 • Qing Qu, Zhihui Zhu, Xiao Li, Manolis C. Tsakiris, John Wright, René Vidal

The problem of finding the sparsest vector (direction) in a low dimensional subspace can be considered as a homogeneous variant of the sparse recovery problem, which finds applications in robust subspace recovery, dictionary learning, sparse blind deconvolution, and many other problems in signal processing and machine learning.

no code implementations • NeurIPS 2019 • Zhihui Zhu, Tianyu Ding, Daniel Robinson, Manolis Tsakiris, René Vidal

Minimizing a non-smooth function over the Grassmannian appears in many applications in machine learning.

no code implementations • CVPR 2020 • Ambar Pal, Connor Lane, René Vidal, Benjamin D. Haeffele

We also show that the global minimizer for DropBlock can be computed in closed form, and that DropConnect is equivalent to Dropout.

1 code implementation • 9 Oct 2019 • Benjamín Béjar, Ivan Dokmanić, René Vidal

In this paper we study the proximal operator of the mixed $\ell_{1,\infty}$ matrix norm and show that it can be computed in closed form by applying the well-known soft-thresholding operator to each column of the matrix.

no code implementations • 2 Aug 2019 • Guilherme França, Daniel P. Robinson, René Vidal

We show that similar discretization schemes applied to Newton's equation with an additional dissipative force, which we refer to as accelerated gradient flow, allow us to obtain accelerated variants of all these proximal algorithms -- the majority of which are new although some recover known cases in the literature.

no code implementations • ECCV 2020 • Effrosyni Mavroudi, Benjamín Béjar Haro, René Vidal

To capture this rich visual and semantic context, we propose using two graphs: (1) an attributed spatio-temporal visual graph whose nodes correspond to actors and objects and whose edges encode different types of interactions, and (2) a symbolic graph that models semantic relationships.

Ranked #10 on Action Detection on Charades (using extra training data)

1 code implementation • NeurIPS 2020 • Guilherme França, Jeremias Sulam, Daniel P. Robinson, René Vidal

Arguably, the two most popular accelerated or momentum-based optimization methods in machine learning are Nesterov's accelerated gradient and Polyaks's heavy ball, both corresponding to different discretizations of a particular second order differential equation with friction.

no code implementations • 17 Aug 2018 • Chun-Guang Li, Chong You, René Vidal

In this paper, we develop a novel geometric analysis for a variant of SSC, named affine SSC (ASSC), for the problem of clustering data from a union of affine subspaces.

no code implementations • 13 Aug 2018 • Guilherme França, Daniel P. Robinson, René Vidal

Recently, there has been great interest in connections between continuous-time dynamical systems and optimization methods, notably in the context of accelerated methods for smooth and unconstrained problems.

1 code implementation • 19 Jul 2018 • Siddharth Mahendran, Ming Yang Lu, Haider Ali, René Vidal

We consider the task of estimating the 3D orientation of an object of known category given an image of the object and a bounding box around it.

no code implementations • 15 Jul 2018 • Evan Schwab, Benjamin D. Haeffele, René Vidal, Nicolas Charon

In the classical setting, signals are represented as vectors and the dictionary learning problem is posed as a matrix factorization problem where the data matrix is approximately factorized into a dictionary matrix and a sparse matrix of coefficients.

no code implementations • 29 Jan 2018 • Effrosyni Mavroudi, Divya Bhaskara, Shahin Sefati, Haider Ali, René Vidal

We introduce an end-to-end algorithm for jointly learning the weights of the CRF model, which include action classification and action transition costs, as well as an overcomplete dictionary of mid-level action primitives.

Ranked #5 on Action Segmentation on JIGSAWS

no code implementations • 10 Oct 2017 • Jacopo Cavazza, Connor Lane, Benjamin D. Haeffele, Vittorio Murino, René Vidal

While the resulting regularizer is closely related to a variational form of the nuclear norm, suggesting that dropout may limit the size of the factorization, we show that it is possible to trivially lower the objective value by doubling the size of the factorization.

no code implementations • 21 Jul 2017 • Evan Schwab, René Vidal, Nicolas Charon

Advanced diffusion magnetic resonance imaging (dMRI) techniques, like diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging (HARDI), remain underutilized compared to diffusion tensor imaging because the scan times needed to produce accurate estimations of fiber orientation are significantly longer.

no code implementations • CVPR 2017 • Chong You, Daniel P. Robinson, René Vidal

While outlier detection methods based on robust statistics have existed for decades, only recently have methods based on sparse and low-rank representation been developed along with guarantees of correct outlier detection when the inliers lie in one or more low-dimensional subspaces.

no code implementations • 18 Dec 2016 • Evan Schwab, René Vidal, Nicolas Charon

High angular resolution diffusion imaging (HARDI) can produce better estimates of fiber orientation than the popularly used diffusion tensor imaging, but the high number of samples needed to estimate diffusivity requires longer patient scan times.

no code implementations • 17 Oct 2016 • Chun-Guang Li, Chong You, René Vidal

In this paper, we propose a joint optimization framework --- Structured Sparse Subspace Clustering (S$^3$C) --- for learning both the affinity and the segmentation.

no code implementations • 21 Dec 2015 • Siddharth Mahendran, René Vidal

Image segmentation and 3D pose estimation are two key cogs in any algorithm for scene understanding.

no code implementations • 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) 2015 • Lingling Tao, René Vidal

While automatic feature learning methods such as supervised sparse dictionary learning or neural networks can be applied to learn feature representation and action classifiers jointly, the resulting features are usually uninterpretable.

no code implementations • NeurIPS 2012 • Ehsan Elhamifar, Guillermo Sapiro, René Vidal

Given pairwise dissimilarities between data points, we consider the problem of finding a subset of data points called representatives or exemplars that can efficiently describe the data collection.

no code implementations • NeurIPS 2011 • Ehsan Elhamifar, René Vidal

We propose an algorithm called Sparse Manifold Clustering and Embedding (SMCE) for simultaneous clustering and dimensionality reduction of data lying in multiple nonlinear manifolds.

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