116 citations · 1.1k across the 96 of their papers we have counts for
113 papers
Quantifying VIO Uncertainty
Stephanie Tsuei, Stefano Soatto
We compute the uncertainty of XIVO, a monocular visual-inertial odometry system based on the Extended Kalman Filter, in the presence of Gaussian noise, drift, and attribution error…
Feature Tracks are not Zero-Mean Gaussian
Stephanie Tsuei, Wenjie Mo, Stefano Soatto
In state estimation algorithms that use feature tracks as input, it is customary to assume that the errors in feature track positions are zero-mean Gaussian. Using a combination of…
Stain-invariant self supervised learning for histopathology image analysis
Alexandre Tiard, Alex Wong, David Joon Ho +5
We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin (H&E) stained images of breast cancer. Our method is robust to stain variations…
Not Just Streaks: Towards Ground Truth for Single Image Deraining
Yunhao Ba, Howard Zhang, Ethan Yang +8
We propose a large-scale dataset of real-world rainy and clean image pairs and a method to remove degradations, induced by rain streaks and rain accumulation, from the image. As th…
Graph Spectral Embedding using the Geodesic Betweeness Centrality
Shay Deutsch, Stefano Soatto
We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure. GSE uses the solution of the Sylvest…
Gacs-Korner Common Information Variational Autoencoder
Michael Kleinman, Alessandro Achille, Stefano Soatto +1
We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique t…
Towards Differential Relational Privacy and its use in Question Answering
Simone Bombari, Alessandro Achille, Zijian Wang +6
Memorization of the relation between entities in a dataset can lead to privacy issues when using a trained model for question answering. We introduce Relational Memorization (RM) t…
Omni-DETR: Omni-Supervised Object Detection with Transformers
Pei Wang, Zhaowei Cai, Hao Yang +4
We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for ob…
Mixed Differential Privacy in Computer Vision
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang +3
We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language…
Contrastive Neighborhood Alignment
Pengkai Zhu, Zhaowei Cai, Yuanjun Xiong +4
We present Contrastive Neighborhood Alignment (CNA), a manifold learning approach to maintain the topology of learned features whereby data points that are mapped to nearby represe…
Stereoscopic Universal Perturbations across Different Architectures and Datasets
Zachary Berger, Parth Agrawal, Tian Yu Liu +2
We study the effect of adversarial perturbations of images on deep stereo matching networks for the disparity estimation task. We present a method to craft a single set of perturba…
DIVA: Dataset Derivative of a Learning Task
Yonatan Dukler, Alessandro Achille, Giovanni Paolini +3
We present a method to compute the derivative of a learning task with respect to a dataset. A learning task is a function from a training set to the validation error, which can be…
Learned Uncertainty Calibration for Visual Inertial Localization
Stephanie Tsuei, Stefano Soatto, Paulo Tabuada +1
The widely-used Extended Kalman Filter (EKF) provides a straightforward recipe to estimate the mean and covariance of the state given all past measurements in a causal and recursiv…
Small Lesion Segmentation in Brain MRIs with Subpixel Embedding
Alex Wong, Allison Chen, Yangchao Wu +4
We present a method to segment MRI scans of the human brain into ischemic stroke lesion and normal tissues. We propose a neural network architecture in the form of a standard encod…
SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots
Alexander Schperberg, Stephanie Tsuei, Stefano Soatto +1
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal while avoiding obstacles in…
Unsupervised Depth Completion with Calibrated Backprojection Layers
Alex Wong, Stefano Soatto
We propose a deep neural network architecture to infer dense depth from an image and a sparse point cloud. It is trained using a video stream and corresponding synchronized sparse…
ARCH++: Animation-Ready Clothed Human Reconstruction Revisited
Tong He, Yuanlu Xu, Shunsuke Saito +2
We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles. Our reconstructed avatars are animation-ready and highly realistic, in both the v…
Uniform Sampling over Episode Difficulty
Sébastien M. R. Arnold, Guneet S. Dhillon, Avinash Ravichandran +1
Episodic training is a core ingredient of few-shot learning to train models on tasks with limited labelled data. Despite its success, episodic training remains largely understudied…
Learning Hierarchical Graph Neural Networks for Image Clustering
Yifan Xing, Tong He, Tianjun Xiao +6
We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using a training set of images annotated…
Representation Consolidation for Training Expert Students
Zhizhong Li, Avinash Ravichandran, Charless Fowlkes +3
Traditionally, distillation has been used to train a student model to emulate the input/output functionality of a teacher. A more useful goal than emulation, yet under-explored, is…
Dynamically Grown Generative Adversarial Networks
Lanlan Liu, Yuting Zhang, Jia Deng +1
Recent work introduced progressive network growing as a promising way to ease the training for large GANs, but the model design and architecture-growing strategy still remain under…
Scene Uncertainty and the Wellington Posterior of Deterministic Image Classifiers
Stephanie Tsuei, Aditya Golatkar, Stefano Soatto
We propose a method to estimate the uncertainty of the outcome of an image classifier on a given input datum. Deep neural networks commonly used for image classification are determ…
Harnessing Unrecognizable Faces for Improving Face Recognition
Siqi Deng, Yuanjun Xiong, Meng Wang +2
The common implementation of face recognition systems as a cascade of a detection stage and a recognition or verification stage can cause problems beyond failures of the detector.…
An Adaptive Framework for Learning Unsupervised Depth Completion
Alex Wong, Xiaohan Fei, Byung-Woo Hong +1
We present a method to infer a dense depth map from a color image and associated sparse depth measurements. Our main contribution lies in the design of an annealing process for det…