arXiv AI
Through the Eyes of the Beholder: Biometric and Demographic Conditioning for Multimodal Sexism Detection
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Computer Vision
2d ago
ViD: Vision-Dominant Gender Bias Mitigation for Large Vision-Language Models
The paper introduces ViD, a vision‑dominant gender bias mitigation framework for large vision‑language models. ViD uses causal analysis of attention patterns and dual mechanisms—backdoor adjustment and refined token selection—to suppress bias while preserving reasoning and generation quality. Experiments show a 14.7% reduction in gender bias on FACET and significant improvements on MS COCO image captioning, all without extra training overhead.
arXiv Machine Learning
Jun 18
Fair Cognitive Impairment Detection Through Unlearning
arXiv:2606. 18571v1 Announce Type: new Abstract: Mild Cognitive Impairment (MCI) is a medical condition characterized by a noticeable decline in memory, language, or thinking abilities.
arXiv AI
Sep 1
Vision-Language Models Suppress Female Representations Under Ambiguous Input
arXiv:2605.31556v2 Announce Type: replace-cross Abstract: Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succe...
arXiv Computer Vision
3d ago
A parameter-efficient three-branch architecture for multimodal misinformation detection with limited annotations
arXiv:2512.20257v2 Announce Type: replace Abstract: With the rise of easily accessible generative tools for creating and manipulating multimedia content, the threat of realistic synthetic alterations...
arXiv AI
Jun 30
LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion
arXiv:2606. 29900v1 Announce Type: cross Abstract: Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment.
Hugging Face Trending Papers
Jun 29
LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion
Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitment. Existing approaches often rely on large language models (LLMs) to analyze textual responses of interviewees in AVI.