54 citations · 84 across the 19 of their papers we have counts for
22 papers
An Information-Theoretic Framework for Feature Construction in Out-of-Distribution Detection
Sudeepta Mondal, Xinyi, Xie +2
We present a theory for the construction of out-of-distribution (OOD) detection features for neural networks. We introduce random features for OOD through a novel information-theor…
HAct: Out-of-Distribution Detection with Neural Net Activation Histograms
Sudeepta Mondal, Ganesh Sundaramoorthi
We propose a simple, efficient, and accurate method for detecting out-of-distribution (OOD) data for trained neural networks. We propose a novel descriptor, HAct - activation histo…
StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation
Huizong Yang, Yuxin Sun, Ganesh Sundaramoorthi +1
We present new insights and a novel paradigm (StEik) for learning implicit neural representations (INR) of shapes. In particular, we shed light on the popular eikonal loss used for…
A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks
Yuxin Sun, Dong Lao, Ganesh Sundaramoorthi +1
We discover restrained numerical instabilities in current training practices of deep networks with stochastic gradient descent (SGD), and its variants. We show numerical error (on…
Flow-Guided Video Inpainting with Scene Templates
Dong Lao, Peihao Zhu, Peter Wonka +1
We consider the problem of filling in missing spatio-temporal regions of a video. We provide a novel flow-based solution by introducing a generative model of images in relation to…
Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation
Angira Sharma, Naeemullah Khan, Muhammad Mubashar +2
In this paper we present a novel loss function, called class-agnostic segmentation (CAS) loss. With CAS loss the class descriptors are learned during training of the network. We do…
Shape-Tailored Deep Neural Networks
Naeemullah Khan, Angira Sharma, Ganesh Sundaramoorthi +1
We present Shape-Tailored Deep Neural Networks (ST-DNN). ST-DNN extend convolutional networks (CNN), which aggregate data from fixed shape (square) neighborhoods, to compute descri…
Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation
Angira Sharma, Naeemullah Khan, Ganesh Sundaramoorthi +1
In this paper we present a novel loss function, called class-agnostic segmentation (CAS) loss. With CAS loss the class descriptors are learned during training of the network. We do…
Channel-Directed Gradients for Optimization of Convolutional Neural Networks
Dong Lao, Peihao Zhu, Peter Wonka +1
We introduce optimization methods for convolutional neural networks that can be used to improve existing gradient-based optimization in terms of generalization error. The method re…
Phase Consistent Ecological Domain Adaptation
Yanchao Yang, Dong Lao, Ganesh Sundaramoorthi +1
We introduce two criteria to regularize the optimization involved in learning a classifier in a domain where no annotated data are available, leveraging annotated data in a differe…
Translation Insensitive CNNs
Ganesh Sundaramoorthi, Timothy E. Wang
We address the problem that state-of-the-art Convolution Neural Networks (CNN) classifiers are not invariant to small shifts. The problem can be solved by the removal of sub-sampli…
Minimum Delay Object Detection From Video
Dong Lao, Ganesh Sundaramoorthi
We consider the problem of detecting objects, as they come into view, from videos in an online fashion. We provide the first real-time solution that is guaranteed to minimize the d…
Accelerated PDE's for efficient solution of regularized inversion problems
Minas Benyamin, Jeff Calder, Ganesh Sundaramoorthi +1
We further develop a new framework, called PDE Acceleration, by applying it to calculus of variations problems defined for general functions on , obtaining efficient…
Accelerated Optimization in the PDE Framework: Formulations for the Manifold of Diffeomorphisms
Ganesh Sundaramoorthi, Anthony Yezzi
We consider the problem of optimization of cost functionals on the infinite-dimensional manifold of diffeomorphisms. We present a new class of optimization methods, valid for any o…
Accelerated Optimization in the PDE Framework: Formulations for the Active Contour Case
Anthony Yezzi, Ganesh Sundaramoorthi
Following the seminal work of Nesterov, accelerated optimization methods have been used to powerfully boost the performance of first-order, gradient-based parameter estimation in s…
SurfCut: Surfaces of Minimal Paths From Topological Structures
Marei Algarni, Ganesh Sundaramoorthi
We present SurfCut, an algorithm for extracting a smooth, simple surface with an unknown 3D curve boundary from a noisy 3D image and a seed point. Our method is built on the novel…
Quickest Moving Object Detection
Dong Lao, Ganesh Sundaramoorthi
We present a general framework and method for simultaneous detection and segmentation of an object in a video that moves (or comes into view of the camera) at some unknown time in…
Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces
Ganesh Sundaramoorthi, Naeemullah Khan, Byung-Woo Hong
We formulate a general energy and method for segmentation that is designed to have preference for segmenting the coarse structure over the fine structure of the data, without smoot…
Tracking via Motion Estimation with Physically Motivated Inter-Region Constraints
Omar Arif, Ganesh Sundaramoorthi, Byung-Woo Hong +1
In this paper, we propose a method for tracking structures (e.g., ventricles and myocardium) in cardiac images (e.g., magnetic resonance) by propagating forward in time a previous…
Matching Through Features and Features Through Matching
Ganesh Sundaramoorthi, Yanchao Yang
This paper addresses how to construct features for the problem of image correspondence, in particular, the paper addresses how to construct features so as to maintain the right lev…
Shape Tracking With Occlusions via Coarse-To-Fine Region-Based Sobolev Descent
Yanchao Yang, Ganesh Sundaramoorthi
We present a method to track the precise shape of an object in video based on new modeling and optimization on a new Riemannian manifold of parameterized regions. Joint dynamic sha…
Sobolev--type metrics in the space of curves
A. C. G. Mennucci, A. Yezzi, G. Sundaramoorthi
We define a manifold where objects are curves, which we parameterize as (, is the circle). Given a curve , we define the tangent space…