The paper evaluates CLIP’s zero‑shot gender estimation on full‑face and periocular images from the Adience dataset. Using image‑text similarity with male/female prompts, CLIP achieves 95.54% accuracy on full faces without task‑specific training. Periocular predictions are initially biased toward males, but threshold alignment improves accuracy to 85.29%, and a linear SVM on CLIP features yields a modest 86.17% accuracy, slightly better than prior Adience results.
arXiv:2512. 00807v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) inherit significant social biases from their training data, notably in gender representation.
arXiv:2609.15608v1 Announce Type: cross Abstract: Detecting sexism on the internet is a fundamentally subjective task; our team, VANGUARD, addresses this challenge in the EXIST 2026 Task 2 by proposi...
arXiv:2606. 03214v1 Announce Type: new Abstract: In this study, we evaluate the performance of skin lesion classification using ResNet-based convolutional models, focusing on the impact of demographic bias in training data, particularly variations in patient sex and age.
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...
The paper investigates how in‑context learning (ICL) in large vision‑language models (LVLMs) can amplify gender bias. Using the VL‑BICLE framework, the authors show that gendered ICL demonstrations shift model bias toward the demonstrated gender, especially in tasks involving gendered language such as image captioning and pronoun prediction. They find that similarity‑based retrieval does not mitigate this bias and that replacing real images with synthetic ones from stable diffusion reduces bias without hurting caption quality.
arXiv:2609.36628v1 Announce Type: new Abstract: Vision-Language Models (VLMs) can generate rich video captions, yet often misidentify which person performs an action or which limb is involved, partic...
Face--voice association models may rely on language or gender cues in the voice rather than on speaker-specific voice characteristics, which can lead to a performance deterioration when the model has...
arXiv:2607. 28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space.
arXiv:2609.17913v1 Announce Type: new Abstract: Face--voice association models may rely on language or gender cues in the voice rather than on speaker-specific voice characteristics, which can lead t...
arXiv:2607. 26104v1 Announce Type: cross Abstract: A person's physical characteristics such as weight and height are important indicators of his physical and mental health, daily life routines and finances.
The paper investigates why reducing the modality gap between image and text representations in CLIP does not always improve zero‑shot classification accuracy. It shows that while average alignment improves, the relative decision margins among classes can shift, leading to a prediction‑level hubness where predictions concentrate on a few classes. Experiments across datasets confirm that accuracy drops correlate with increased prediction concentration for both linear and learning‑based gap corrections.
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.