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Found 21 papers, 13 papers with code
Non-Linear Outlier Synthesis for Out-of-Distribution Detection
The reliability of supervised classifiers is severely hampered by their limitations in dealing with unexpected inputs, leading to great interest in out-of-distribution (OOD) detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Targeted Visual Prompting for Medical Visual Question Answering
With growing interest in recent years, medical visual question answering (Med-VQA) has rapidly evolved, with multimodal large language models (MLLMs) emerging as an alternative to classical model architectures.
Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detection
The inability of deep learning models to handle data drawn from unseen distributions has sparked much interest in unsupervised out-of-distribution (U-OOD) detection, as it is crucial for reliable deep learning models.
Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models
Here, we report the development of a generative AI method capable of predicting optical galaxy spectra from photometric broad-band images alone.
Continual Unsupervised Out-of-Distribution Detection
To tackle this new setting, we propose a method that starts from a U-OOD detector, which is agnostic to the OOD distribution, and slowly updates during deployment to account for the actual OOD distribution.
Masked Image Modelling for retinal OCT understanding
To this end, we leverage Masked Autoencoders (MAE), a simple and scalable method for self-supervised learning, to obtain a powerful and general representation for OCT images by training on 700K OCT images from 41K patients collected under real world clinical settings.
Hyperbolic Random Forests
To make hyperbolic random forests work on multi-class data and imbalanced experiments, we furthermore outline a new method for combining classes based on their lowest common ancestor and a class-balanced version of the large-margin loss.
Localized Questions in Medical Visual Question Answering
Our code and data are available at https://github. com/sergiotasconmorales/locvqa.
Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery
Detecting so-called out-of-distribution (OoD) samples is crucial in safety-critical applications such as robotically guided retinal microsurgery, where distances between the instrument and the retina are derived from sequences of 1D images that are acquired by an instrument-integrated optical coherence tomography (iiOCT) probe.
Logical Implications for Visual Question Answering Consistency
Despite considerable recent progress in Visual Question Answering (VQA) models, inconsistent or contradictory answers continue to cast doubt on their true reasoning capabilities.
Stochastic Segmentation with Conditional Categorical Diffusion Models
Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurately matches the image's content may not be suitable for safety-critical domains such as medical diagnostics and autonomous driving.
ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection
In this work, we focus on applying our method to the detection of AGN candidates in a Sloan Digital Sky Survey galaxy sample, since the identification and classification of Active Galactic Nuclei (AGN) in the optical band still remains a challenging task in extragalactic astronomy.
Consistency-preserving Visual Question Answering in Medical Imaging
Visual Question Answering (VQA) models take an image and a natural-language question as input and infer the answer to the question.
Data Invariants to Understand Unsupervised Out-of-Distribution Detection
Motivated by a simple thought experiment, we propose a characterization of U-OOD based on the invariants of the training dataset.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Real-Time Camera Pose Estimation for Sports Fields
Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera.
Fused Detection of Retinal Biomarkers in OCT Volumes
In this context, we present a method that automatically predicts the presence of biomarkers in OCT cross-sections by incorporating information from the entire volume.
Simulation of hyperelastic materials in real-time using Deep Learning
In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm.
Self-Binarizing Networks
To obtain similar binary networks, existing methods rely on the sign activation function.
Imposing Hard Constraints on Deep Networks: Promises and Limitations
Imposing constraints on the output of a Deep Neural Net is one way to improve the quality of its predictions while loosening the requirements for labeled training data.
Uniform Information Segmentation
Since information is a natural way of measuring image complexity, our proposed algorithm leads to image segments that are smaller and denser in areas of high complexity and larger in homogeneous regions, thus simplifying the image while preserving its details.
Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation
Most recent approaches to monocular 3D human pose estimation rely on Deep Learning.
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