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Found 50 papers, 18 papers with code
QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex
Existing transformer-based forecasting methods, while effective in many domains, struggle to capture the distinctiveness of neural signals characterized by spatiotemporal sparsity and intricate dependencies.
Self-supervised learning for radio-astronomy source classification: a benchmark
This study explores the application of SSL to radio astronomy, comparing the performance of SSL-trained models with that of traditional models pretrained on natural images, evaluating the importance of data curation for SSL, and assessing the potential benefits of self-supervision to different domain-specific radio astronomy datasets.
Evidential Federated Learning for Skin Lesion Image Classification
We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification.
FedRewind: Rewinding Continual Model Exchange for Decentralized Federated Learning
In this paper, we present FedRewind, a novel approach to decentralized federated learning that leverages model exchange among nodes to address the issue of data distribution shift.
IPMN Risk Assessment under Federated Learning Paradigm
In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset.
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks.
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences.
Large-Scale Multi-Center CT and MRI Segmentation of Pancreas with Deep Learning
We also collected CT scans of 1, 350 patients from publicly available sources for benchmarking purposes.
Diffexplainer: Towards Cross-modal Global Explanations with Diffusion Models
We present DiffExplainer, a novel framework that, leveraging language-vision models, enables multimodal global explainability.
SalFoM: Dynamic Saliency Prediction with Video Foundation Models
Recent advancements in video saliency prediction (VSP) have shown promising performance compared to the human visual system, whose emulation is the primary goal of VSP.
Selective Attention-based Modulation for Continual Learning
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting.
Transformer-based Video Saliency Prediction with High Temporal Dimension Decoding
In recent years, finding an effective and efficient strategy for exploiting spatial and temporal information has been a hot research topic in video saliency prediction (VSP).
Wake-Sleep Consolidated Learning
In the REM stage, the model is exposed to previously-unseen realistic visual sensory experience, and the dreaming process is activated, which enables the model to explore the potential feature space, thus preparing synapses to future knowledge.
Domain Generalization with Fourier Transform and Soft Thresholding
However, it neglects background interference in the amplitude spectrum.
Radiomics Boosts Deep Learning Model for IPMN Classification
We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field.
MatFuse: Controllable Material Generation with Diffusion Models
Creating high-quality materials in computer graphics is a challenging and time-consuming task, which requires great expertise.
Car-Driver Drowsiness Assessment through 1D Temporal Convolutional Networks
Recently, the scientific progress of Advanced Driver Assistance System solutions (ADAS) has played a key role in enhancing the overall safety of driving.
Visual Saliency Detection in Advanced Driver Assistance Systems
A dedicated 1D temporal deep convolutional network has been devised to classify the collected PPG time-series, enabling us to assess the driver level of attentiveness.
Non-Linear Self Augmentation Deep Pipeline for Cancer Treatment outcome Prediction
Immunotherapy emerges as promising approach for treating cancer.
A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications
Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution.
RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation
We evaluate the effectiveness of this approach by training a semantic segmentation model on a real dataset augmented in two ways: 1) using synthetic images obtained from real masks, and 2) generating images from synthetic semantic masks.
MeT: A Graph Transformer for Semantic Segmentation of 3D Meshes
Polygonal meshes have become the standard for discretely approximating 3D shapes, thanks to their efficiency and high flexibility in capturing non-uniform shapes.
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning
Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically.
A baseline on continual learning methods for video action recognition
Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervisedly-trained models.
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods
In recent years, deep learning has been successfully applied in various scientific domains.
Transformer-based Image Generation from Scene Graphs
In this work, we show how employing multi-head attention to encode the graph information, as well as using a transformer-based model in the latent space for image generation can improve the quality of the sampled data, without the need to employ adversarial models with the subsequent advantage in terms of training stability.
TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation
Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications.
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning
By means of extensive experiments, we show that applying LiDER delivers a stable performance gain to several state-of-the-art rehearsal CL methods across multiple datasets, both in the presence and absence of pre-training.
Transforming Image Generation from Scene Graphs
Generating images from semantic visual knowledge is a challenging task, that can be useful to condition the synthesis process in complex, subtle, and unambiguous ways, compared to alternatives such as class labels or text descriptions.
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
Early detection of precancerous cysts or neoplasms, i. e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome.
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis
In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data.
Effects of Auxiliary Knowledge on Continual Learning
In this paper we propose a new, simple, CL algorithm that focuses on solving the current task in a way that might facilitate the learning of the next ones.
Transfer without Forgetting
This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL).
Hierarchical 3D Feature Learning for Pancreas Segmentation
We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans.
Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 AI diagnosis
This is the leitmotif of the convergence between HPC and AI.
SurfaceNet: Adversarial SVBRDF Estimation from a Single Image
In this paper we present SurfaceNet, an approach for estimating spatially-varying bidirectional reflectance distribution function (SVBRDF) material properties from a single image.
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.
An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans
In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans.
Correct block-design experiments mitigate temporal correlation bias in EEG classification
We argue that the reason why Li et al. [1] observe such high correlation in EEG data is their unconventional experimental design and settings that violate the basic cognitive neuroscience design recommendations, first and foremost the one of limiting the experiments' duration, as instead done in [2].
Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction
When the base hierarchical model is empowered with domain-specific modules, performance improves, outperforming state-of-the-art models on three out of five metrics on the DHF1K benchmark and reaching the second-best results on the other two.
Domain Adaptation for Outdoor Robot Traversability Estimation from RGB data with Safety-Preserving Loss
Being able to estimate the traversability of the area surrounding a mobile robot is a fundamental task in the design of a navigation algorithm.
Diagnosing Colorectal Polyps in the Wild with Capsule Networks
In this study, we design a novel capsule network architecture (D-Caps) to improve the viability of optical biopsy of colorectal polyps.
Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features
After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations.
Semi Supervised Semantic Segmentation Using Generative Adversarial Network
Semantic segmentation has been a long standing challenging task in computer vision.
Generative Adversarial Networks Conditioned by Brain Signals
In this work, we build on the latter class of approaches and investigate the possibility of driving and conditioning the image generation process by means of brain signals recorded, through an electroencephalograph (EEG), while users look at images from a set of 40 ImageNet object categories with the objective of generating the seen images.
Top-Down Saliency Detection Driven by Visual Classification
This paper presents an approach for top-down saliency detection guided by visual classification tasks.
Semi and Weakly Supervised Semantic Segmentation Using Generative Adversarial Network
Semantic segmentation has been a long standing challenging task in computer vision.
Deep Learning Human Mind for Automated Visual Classification
In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories.
Gamifying Video Object Segmentation
Video object segmentation can be considered as one of the most challenging computer vision problems.
Superpixel-Based Video Object Segmentation Using Perceptual Organization and Location Prior
In this paper we present an approach for segmenting objects in videos taken in complex scenes with multiple and different targets.
