Search Results for author:
Found 17 papers, 3 papers with code
AeroLite: Tag-Guided Lightweight Generation of Aerial Image Captions
Accurate and automated captioning of aerial imagery is crucial for applications like environmental monitoring, urban planning, and disaster management.
Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging
In this paper, we propose PATH-X, a framework that integrates Vision Transformers (ViT) and Autoencoders with SHAP (Shapley Additive Explanations) to enhance model explainability for patient stratification and risk prediction using WSIs from The Cancer Genome Atlas (TCGA).
Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing
Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in zero-shot segmentation tasks.
Explainable AI Methods for Multi-Omics Analysis: A Survey
Advancements in high-throughput technologies have led to a shift from traditional hypothesis-driven methodologies to data-driven approaches.
ALMRR: Anomaly Localization Mamba on Industrial Textured Surface with Feature Reconstruction and Refinement
Unsupervised anomaly localization on industrial textured images has achieved remarkable results through reconstruction-based methods, yet existing approaches based on image reconstruction and feature reconstruc-tion each have their own shortcomings.
VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation
In this end, we propose a visual context prompting model (VCP-CLIP) for ZSAS task based on CLIP.
Ranked #8 on
Anomaly Detection
on VisA
(Segmentation AUPRO metric, using extra
training data)
Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition
Image recognition techniques heavily rely on abundant labeled data, particularly in medical contexts.
DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)
Influence Maximization is the task of selecting optimal nodes maximising the influence spread in social networks.
SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation
This paper proposes a novel self-supervised based Cut-and-Paste GAN to perform foreground object segmentation and generate realistic composite images without manual annotations.
An Emotion-guided Approach to Domain Adaptive Fake News Detection using Adversarial Learning
Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance.
Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation
Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance.
A Spreader Ranking Algorithm for Extremely Low-budget Influence Maximization in Social Networks using Community Bridge Nodes
In this work, we propose a community structures-based approach, which employs a K-Shell algorithm in order to generate a score for the connections between seed nodes and communities for low-budget scenarios.
The Effectiveness of Social Media Engagement Strategy on Disaster Fundraising
Social media has been a powerful tool and an integral part of communication, especially during natural disasters.
On the utility of power spectral techniques with feature selection techniques for effective mental task classification in noninvasive BCI
In the case of mental task classification, the availability of training samples to features are minimal.
Stock Market Analysis with Text Data: A Review
Then, we cover the analysis techniques and create a taxonomy of the main stock market forecast models.
A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models
In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS).
Preference Neural Network
This paper proposes a preference neural network (PNN) to address the problem of indifference preferences orders with new activation function.
