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Found 84 papers, 15 papers with code
Vision Transformers with Natural Language Semantics
Typically, ViT tokens are associated with rectangular image patches that lack specific semantic context, making interpretation difficult and failing to effectively encapsulate information.
Federated Fairness without Access to Sensitive Groups
Current approaches to group fairness in federated learning assume the existence of predefined and labeled sensitive groups during training.
Simulation-based Inference for Cardiovascular Models
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico.
Robust Hybrid Learning With Expert Augmentation
Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data.
Minimax Demographic Group Fairness in Federated Learning
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models.
Federating for Learning Group Fair Models
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models.
A Joint Subspace View to Convolutional Neural Networks
In other words, a CNN is now reduced to layers of filter atoms, typically a few hundred of parameters per layer, with a common block of subspace coefficients shared across layers.
Adversarial Factor Models for the Generation of Improved Autism Diagnostic Biomarkers
Second, we show this same model can be used to learn a disentangled representation of multimodal biomarkers that results in an increase in predictive performance.
GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions
Generating videos from text is a challenging task due to its high computational requirements for training and infinite possible answers for evaluation.
Ranked #16 on
Text-to-Video Generation
on MSR-VTT
(CLIPSIM metric)
Blind Pareto Fairness and Subgroup Robustness
With the wide adoption of machine learning algorithms across various application domains, there is a growing interest in the fairness properties of such algorithms.
ReaPER: Improving Sample Efficiency in Model-Based Latent Imagination
Deep Reinforcement Learning (DRL) can distill behavioural policies from sensory input that solve complex tasks, however, the policies tend to be task-specific and sample inefficient, requiring a large number of interactions with the environment that may be costly or impractical for many real world applications.
Cirrus: A Long-range Bi-pattern LiDAR Dataset
Point density varies significantly across such a long range, and different scanning patterns further diversify object representation in LiDAR.
Instance-based Generalization in Reinforcement Learning
Agents trained via deep reinforcement learning (RL) routinely fail to generalize to unseen environments, even when these share the same underlying dynamics as the training levels.
Using Text to Teach Image Retrieval
Image retrieval relies heavily on the quality of the data modeling and the distance measurement in the feature space.
Minimax Pareto Fairness: A Multi Objective Perspective
In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective.
Instance based Generalization in Reinforcement Learning
Agents trained via deep reinforcement learning (RL) routinely fail to generalize to unseen environments, even when these share the same underlying dynamics as the training levels.
ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution
We then explicitly regularize CNN kernels by enforcing decomposed coefficients to be shared across sub-structures, while leaving each sub-structure only its own dictionary atoms, a few hundreds of parameters typically, which leads to dramatic model reductions.
Nested Learning For Multi-Granular Tasks
Standard deep neural networks (DNNs) are commonly trained in an end-to-end fashion for specific tasks such as object recognition, face identification, or character recognition, among many examples.
Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method
Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e. g., to spoofing attacks and low-light conditions.
Fairness With Minimal Harm: A Pareto-Optimal Approach For Healthcare
Common fairness definitions in machine learning focus on balancing notions of disparity and utility.
SalGaze: Personalizing Gaze Estimation Using Visual Saliency
Traditional gaze estimation methods typically require explicit user calibration to achieve high accuracy.
Range Adaptation for 3D Object Detection in LiDAR
To the best of our knowledge, this paper is the first attempt to study cross-range LiDAR adaptation for object detection in point clouds.
Scale-Equivariant Neural Networks with Decomposed Convolutional Filters
Encoding the input scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many vision tasks especially when dealing with multiscale input signals.
Pareto Optimality in No-Harm Fairness
Common fairness definitions in machine learning focus on balancing various notions of disparity and utility.
A Dictionary Approach to Domain-Invariant Learning in Deep Networks
In this paper, we consider domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Network (CNN).
Stochastic Conditional Generative Networks with Basis Decomposition
One of these questions is how to efficiently achieve proper diversity and sampling of the multi-mode data space.
Domain-invariant Learning using Adaptive Filter Decomposition
Domain shifts are frequently encountered in real-world scenarios.
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters
Encoding the scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many computer vision tasks especially when dealing with multiscale inputs.
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
We use influence functions to measure the impact of every training sample on the validation set data.
Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations
The DC and cDC for automatic STN segmentation were 0. 66 and 0. 80, respectively.
Learning data-derived privacy preserving representations from information metrics
We study space-preserving transformations where the utility provider can use the same algorithm on original and sanitized data, a critical and novel attribute to help service providers accommodate varying privacy requirements with a single set of utility algorithms.
Non-contact photoplethysmogram and instantaneous heart rate estimation from infrared face video
One way to avoid these constraints is using infrared cameras, allowing the monitoring of iHR under low light conditions.
OLÃ: Orthogonal Low-Rank Embedding - A Plug and Play Geometric Loss for Deep Learning
Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification.
Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning
Deep neural networks (DNNs) typically have enough capacity to fit random data by brute force even when conventional data-dependent regularizations focusing on the geometry of the features are imposed.
Learning to Collaborate for User-Controlled Privacy
As such, users and utility providers should collaborate in data privacy, a paradigm that has not yet been developed in the privacy research community.
DNN or k-NN: That is the Generalize vs. Memorize Question
Moreover, the behavior of DNNs compared to the linear classifiers SVM and LR is quite the same on the training and test data regardless of whether the network generalizes.
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
Explicit encoding of group actions in deep features makes it possible for convolutional neural networks (CNNs) to handle global deformations of images, which is critical to success in many vision tasks.
Liveness Detection Using Implicit 3D Features
Spoofing attacks are a threat to modern face recognition systems.
Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification
In this paper we introduce an ensemble method for convolutional neural network (CNN), called "virtual branching," which can be implemented with nearly no additional parameters and computation on top of standard CNNs.
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
In this paper, we suggest to decompose convolutional filters in CNN as a truncated expansion with pre-fixed bases, namely the Decomposed Convolutional Filters network (DCFNet), where the expansion coefficients remain learned from data.
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning
Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification.
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests, with near-optimal information-theoretic code aggregation among trees.
LDMNet: Low Dimensional Manifold Regularized Neural Networks
To resolve this, we propose a new framework, the Low-Dimensional-Manifold-regularized neural Network (LDMNet), which incorporates a feature regularization method that focuses on the geometry of both the input data and the output features.
Deep Video Deblurring for Hand-Held Cameras
We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.
Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems
Confronted with this challenge, in this paper we open a new line of research, where the security, privacy, and fairness is learned and used in a closed environment.
Deep Video Deblurring
We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.
Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model
In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human' annotation involved.
Not Afraid of the Dark: NIR-VIS Face Recognition via Cross-spectral Hallucination and Low-rank Embedding
We observe that it is often equally effective to perform hallucination to input NIR images or low-rank embedding to output deep features for a VIS deep model for cross-spectral recognition.
Probabilistic Fluorescence-Based Synapse Detection
The present work describes new probabilistic image analysis methods for single-synapse analysis of synapse populations in both animal and human brains.
Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting
In this work, we introduce a highly efficient algorithm to address the nonnegative matrix underapproximation (NMU) problem, i. e., nonnegative matrix factorization (NMF) with an additional underapproximation constraint.
Fast L1-NMF for Multiple Parametric Model Estimation
In this work we introduce a comprehensive algorithmic pipeline for multiple parametric model estimation.
Generalization Error of Invariant Classifiers
We show that whereas the generalization error of a non-invariant classifier is proportional to the complexity of the input space, the generalization error of an invariant classifier is proportional to the complexity of the base space.
Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems
Solving inverse problems with iterative algorithms is popular, especially for large data.
Robust Large Margin Deep Neural Networks
The generalization error of deep neural networks via their classification margin is studied in this work.
Fundamental Limits in Multi-image Alignment
In this work, we tackle the problem of finding the performance limits in image registration when multiple shifted and noisy observations are available.
GraphConnect: A Regularization Framework for Neural Networks
The new method encourages the relationships between the learned decisions to resemble a graph representing the manifold structure.
Discriminative Robust Transformation Learning
This paper proposes a framework for learning features that are robust to data variation, which is particularly important when only a limited number of trainingsamples are available.
Geometry-aware Deep Transform
Many recent efforts have been devoted to designing sophisticated deep learning structures, obtaining revolutionary results on benchmark datasets.
Hand-held Video Deblurring via Efficient Fourier Aggregation
In this work, we present an algorithm that removes blur due to camera shake by combining information in the Fourier domain from nearby frames in a video.
Burst Deblurring: Removing Camera Shake Through Fourier Burst Accumulation
Numerous recent approaches attempt to remove image blur due to camera shake, either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem.
Compressed Nonnegative Matrix Factorization is Fast and Accurate
To address this, in this work we propose to use structured random compression, that is, random projections that exploit the data structure, for two NMF variants: classical and separable.
Removing Camera Shake via Weighted Fourier Burst Accumulation
The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude.
Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?
Three important properties of a classification machinery are: (i) the system preserves the core information of the input data; (ii) the training examples convey information about unseen data; and (iii) the system is able to treat differently points from different classes.
Data Representation using the Weyl Transform
The Weyl transform is introduced as a rich framework for data representation.
On the Stability of Deep Networks
In particular, we formally prove in the longer version that DNN with random Gaussian weights perform a distance-preserving embedding of the data, with a special treatment for in-class and out-of-class data.
Random Forests Can Hash
Traditional random forest fails to enforce the consistency of hashes generated from each tree for the same class data, i. e., to preserve the underlying similarity, and it also lacks a principled way for code aggregation across trees.
Dissimilarity-based Sparse Subset Selection
The solution of our optimization finds representatives and the assignment of each element of the target set to each representative, hence, obtaining a clustering.
Graph Matching: Relax at Your Own Risk
Indeed, experimental results illuminate and corroborate these theoretical findings, demonstrating that excellent results are achieved in both benchmark and real data problems by amalgamating the two approaches.
A Bi-clustering Framework for Consensus Problems
We consider grouping as a general characterization for problems such as clustering, community detection in networks, and multiple parametric model estimation.
Low-Cost Compressive Sensing for Color Video and Depth
A simple and inexpensive (low-power and low-bandwidth) modification is made to a conventional off-the-shelf color video camera, from which we recover {multiple} color frames for each of the original measured frames, and each of the recovered frames can be focused at a different depth.
Learning Transformations for Classification Forests
This work introduces a transformation-based learner model for classification forests.
Sparse similarity-preserving hashing
In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing.
Supervised Sparse Analysis and Synthesis Operators
In this paper, we propose a new and computationally efficient framework for learning sparse models.
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
We also present results on multimodal graphs and applications to collaborative inference of brain connectivity from alignment-free functional magnetic resonance imaging (fMRI) data.
Learning Transformations for Clustering and Classification
A low-rank transformation learning framework for subspace clustering and classification is here proposed.
Domain-invariant Face Recognition using Learned Low-rank Transformation
We present a low-rank transformation approach to compensate for face variations due to changes in visual domains, such as pose and illumination.
Learning Robust Subspace Clustering
This proposed learned robust subspace clustering framework significantly enhances the performance of existing subspace clustering methods.
Video Human Segmentation using Fuzzy Object Models and its Application to Body Pose Estimation of Toddlers for Behavior Studies
Video object segmentation is a challenging problem due to the presence of deformable, connected, and articulated objects, intra- and inter-object occlusions, object motion, and poor lighting.
Adaptive Temporal Compressive Sensing for Video
This paper introduces the concept of adaptive temporal compressive sensing (CS) for video.
Topology Constraints in Graphical Models
Graphical models are a very useful tool to describe and understand natural phenomena, from gene expression to climate change and social interactions.
Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery
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.
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
A general framework for solving image inverse problems is introduced in this paper.
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations
The beta process is employed as a prior for learning the dictionary, and this non-parametric method naturally infers an appropriate dictionary size.
Supervised Dictionary Learning
It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data.
