research
∙
08/15/2023
A Foundation LAnguage-Image model of the Retina (FLAIR): Encoding expert knowledge in text supervision
Foundation vision-language models are currently transforming computer vi...
share
research
∙
07/21/2023
Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Images
Training a deep learning model to classify histopathological images is c...
research
∙
04/13/2023
Task Adaptive Feature Transformation for One-Shot Learning
We introduce a simple non-linear embedding adaptation layer, which is fi...
research
∙
03/29/2023
Transductive few-shot adapters for medical image segmentation
With the recent raise of foundation models in computer vision and NLP, t...
research
∙
03/28/2023
TFS-ViT: Token-Level Feature Stylization for Domain Generalization
Standard deep learning models such as convolutional neural networks (CNN...
research
∙
01/20/2023
Open-Set Likelihood Maximization for Few-Shot Learning
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classi...
research
∙
12/13/2022
Connectivity-constrained Interactive Panoptic Segmentation
We address interactive panoptic annotation, where one segment all object...
research
∙
12/01/2022
Mutual Information-based Generalized Category Discovery
We introduce an information-maximization approach for the Generalized Ca...
research
∙
11/28/2022
Class Adaptive Network Calibration
Recent studies have revealed that, beyond conventional accuracy, calibra...
research
∙
11/25/2022
A Strong Baseline for Generalized Few-Shot Semantic Segmentation
This paper introduces a generalized few-shot segmentation framework with...
research
∙
10/26/2022
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference
Standard few-shot benchmarks are often built upon simplifying assumption...
research
∙
10/20/2022
TTTFlow: Unsupervised Test-Time Training with Normalizing Flow
A major problem of deep neural networks for image classification is thei...
research
∙
07/30/2022
Simplex Clustering via sBeta with Applications to Online Adjustment of Black-Box Predictions
We explore clustering the softmax predictions of deep neural networks an...
research
∙
06/18/2022
Model-Agnostic Few-Shot Open-Set Recognition
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classi...
research
∙
06/14/2022
Proximal Splitting Adversarial Attacks for Semantic Segmentation
Classification has been the focal point of research on adversarial attac...
research
∙
05/31/2022
FHIST: A Benchmark for Few-shot Classification of Histological Images
Few-shot learning has recently attracted wide interest in image classifi...
research
∙
05/16/2022
Test-Time Adaptation with Shape Moments for Image Segmentation
Supervised learning is well-known to fail at generalization under distri...
research
∙
04/24/2022
Realistic Evaluation of Transductive Few-Shot Learning
Transductive inference is widely used in few-shot learning, as it levera...
research
∙
01/15/2022
Parameter-free Online Test-time Adaptation
Training state-of-the-art vision models has become prohibitively expensi...
research
∙
11/30/2021
The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration
In spite of the dominant performances of deep neural networks, recent wo...
research
∙
09/21/2021
Mixed-supervised segmentation: Confidence maximization helps knowledge distillation
Despite achieving promising results in a breadth of medical image segmen...
research
∙
08/06/2021
Source-Free Domain Adaptation for Image Segmentation
Domain adaptation (DA) has drawn high interest for its capacity to adapt...
research
∙
07/29/2021
A Preconditioned Alternating Minimization Framework for Nonconvex and Half Quadratic Optimization
For some typical and widely used non-convex half-quadratic regularizatio...
research
∙
06/23/2021
Mutual-Information Based Few-Shot Classification
We introduce Transductive Infomation Maximization (TIM) for few-shot lea...
research
∙
06/12/2021
Adversarial Robustness via Fisher-Rao Regularization
Adversarial robustness has become a topic of growing interest in machine...
research
∙
05/03/2021
Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!
Standard losses for training deep segmentation networks could be seen as...
research
∙
04/18/2021
The hidden label-marginal biases of segmentation losses
Most segmentation losses are arguably variants of the Cross-Entropy (CE)...
research
∙
12/15/2020
Teach me to segment with mixed supervision: Confident students become masters
Deep segmentation neural networks require large training datasets with p...
research
∙
12/11/2020
Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
Few-shot segmentation has recently attracted substantial interest, with ...
research
∙
12/09/2020
AIDE: Annotation-efficient deep learning for automatic medical image segmentation
Accurate image segmentation is crucial for medical imaging applications....
research
∙
10/01/2020
Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images
Assessing the degree of disease severity in biomedical images is a task ...
research
∙
09/03/2020
The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
The segmentation of the retinal vasculature from eye fundus images repre...
research
∙
08/25/2020
Transductive Information Maximization For Few-Shot Learning
We introduce Transductive Infomation Maximization (TIM) for few-shot lea...
research
∙
06/25/2020
Learning Data Augmentation with Online Bilevel Optimization for Image Classification
Data augmentation is a key practice in machine learning for improving ge...
research
∙
05/07/2020
Source-Relaxed Domain Adaptation for Image Segmentation
Domain adaptation (DA) has drawn high interests for its capacity to adap...
research
∙
03/18/2020
Semi-supervised few-shot learning for medical image segmentation
Recent years have witnessed the great progress of deep neural networks o...
research
∙
03/09/2020
On the Texture Bias for Few-Shot CNN Segmentation
Despite the initial belief that Convolutional Neural Networks (CNNs) are...
research
∙
09/21/2019
Adversarial Learning of General Transformations for Data Augmentation
Data augmentation (DA) is fundamental against overfitting in large convo...
research
∙
09/08/2019
Deep weakly-supervised learning methods for classification and localization in histology images: a survey
Using state-of-the-art deep learning models for the computer-assisted di...
research
∙
08/15/2019
Discretely-constrained deep network for weakly supervised segmentation
An efficient strategy for weakly-supervised segmentation is to impose co...
research
∙
08/08/2019
Universal Adversarial Audio Perturbations
We demonstrate the existence of universal adversarial perturbations, whi...
research
∙
08/08/2019
Constrained domain adaptation for segmentation
We propose to adapt segmentation networks with a constrained formulation...
research
∙
07/25/2019
Weakly Supervised Object Localization using Min-Max Entropy: an Interpretable Framework
Weakly supervised object localization (WSOL) models aim to locate object...
research
∙
07/04/2019
A Survey of Pruning Methods for Efficient Person Re-identification Across Domains
Recent years have witnessed a substantial increase in the deep learning ...
research
∙
06/19/2019
Clustering with Fairness Constraints: A Flexible and Scalable Approach
This study investigates a general variational formulation of fair cluste...
research
∙
04/10/2019
Curriculum semi-supervised segmentation
This study investigates a curriculum-style strategy for semi-supervised ...
research
∙
04/08/2019
Log-barrier constrained CNNs
This study investigates imposing inequality constraints on the outputs o...
research
∙
04/04/2019
On Direct Distribution Matching for Adapting Segmentation Networks
Minimization of distribution matching losses is a principled approach to...
research
∙
12/17/2018
Boundary loss for highly unbalanced segmentation
Widely used loss functions for convolutional neural network (CNN) segmen...
research
∙
11/23/2018
