Cultural Competence

Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration

Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning capabilities but remain highly susceptible to object hallucination, where models generate responses that are not factually aligned with the visual content. Recent works attribute this issue to an inherent bias of LVLMs where vision token attention map has a fixed correlation with spatial position, and propose to mitigat...

FCBoost-Net: A Generative Network for Synthesizing Multiple Collocated Outfits via Fashion Compatibility Boosting

Outfit generation is a challenging task in the field of fashion technology, in which the aim is to create a collocated set of fashion items that complement a given set of items. Previous studies in this area have been limited to generating a unique set of fashion items based on a given set of items, without providing additional options to users. This lack of a diverse range of choices necessitat...

Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation

Feature selection is an important but challenging task in causal inference for obtaining unbiased estimates of causal quantities. Properly selected ...

Do Audio-Visual Segmentation Models Truly Segment Sounding Objects?

Unlike traditional visual segmentation, audio-visual segmentation (AVS) requires the model not only to identify and segment objects but also to dete...

ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition

Bias in machine learning models can lead to unfair decision making, and while it has been well-studied in the image and text domains, it remains und...

Machine Learning Fairness for Depression Detection using EEG Data

This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We ...

Cross-Language Approach for Quranic QA

Question answering systems face critical limitations in languages with limited resources and scarce data, making the development of robust models es...

BAG: Body-Aligned 3D Wearable Asset Generation

While recent advancements have shown remarkable progress in general 3D shape generation models, the challenge of leveraging these approaches to auto...

Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?

Text-to-Image (T2I) models have recently gained significant attention due to their ability to generate high-quality images and are consequently used...

GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree search

Motivation: Nucleocytoplasmic large DNA viruses (NCLDVs) are notable for their large genomes and extensive gene repertoires, which contribute to the...

FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting

This paper presents \textbf{FreEformer}, a simple yet effective model that leverages a \textbf{Fre}quency \textbf{E}nhanced Trans\textbf{former} for...

ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild

The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has signif...

A Comprehensive Social Bias Audit of Contrastive Vision Language Models

In the domain of text-to-image generative models, biases inherent in training datasets often propagate into generated content, posing significant et...

Reducing Size Bias in Sampling for Infectious Disease Spread on Networks

Epidemiological models can aid policymakers in reducing disease spread by predicting outcomes based on disease dynamics and contact network characte...

Academic case reports lack diversity: Assessing the presence and diversity of sociodemographic and behavioral factors related to Post COVID-19 Condition

Understanding the prevalence, disparities, and symptom variations of Post COVID-19 Condition (PCC) for vulnerable populations is crucial to improvin...

Owls are wise and foxes are unfaithful: Uncovering animal stereotypes in vision-language models

Animal stereotypes are deeply embedded in human culture and language. They often shape our perceptions and expectations of various species. Our stud...

Zero-shot Bias Correction: Efficient MR Image Inhomogeneity Reduction Without Any Data

In recent years, deep neural networks for image inhomogeneity reduction have shown promising results. However, current methods with (un)supervised s...

On the "Illusion" of Gender Bias in Face Recognition: Explaining the Fairness Issue Through Non-demographic Attributes

Face recognition systems (FRS) exhibit significant accuracy differences based on the user's gender. Since such a gender gap reduces the trustworthin...

Are generative models fair? A study of racial bias in dermatological image generation

Racial bias in medicine, such as in dermatology, presents significant ethical and clinical challenges. This is likely to happen because there is a s...

MASS: Overcoming Language Bias in Image-Text Matching

Pretrained visual-language models have made significant advancements in multimodal tasks, including image-text retrieval. However, a major challenge...