Gender Bias in Vision-Language In-Context Learning

arXiv:2609.27682 · cs.CV · Submitted 2026-09-23 · Read on arXiv

cs.CV

Submitted: 2026-09-23

Updated: 2026-09-28

Code: https://github.com/mathfather/Gender-Bias-in-VL-ICL

Terminology

• In-Context Learning — 39× in this paper · explained in 4 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• In-Context Learning (ICL) — 2× in this paper · explained in 5 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Similarity-based Image-Image Retrieval — 2× in this paper
• Similarity-based Image-Text Retrieval — 2× in this paper
• Steering — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Balanced Sample — 1× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Chain-of-Thought (CoT) Reasoning — 1× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• DCI Densely Captioned Images — 1× in this paper
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Female-only Sample — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Inference-Time Steering — 1× in this paper · explained in 2 episodes
• Male-only Sample — 1× in this paper
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Random Sample — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• Microsoft COCO Captions: Data Collection and Evaluation Server
• Debiasing Vision-Language Models via Biased Prompts
• Towards Multimodal In-Context Learning for Vision & Language Models
• Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals
• Building and better understanding vision-language models: insights and future directions
• MIMIC-IT: Multi-Modal In-Context Instruction Tuning
• Advancing Multimodal In-Context Learning in Large Vision-Language Models with Task-aware Demonstrations
• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
• On the Limitations of Steering in Language Model Alignment
• GPT-4 Technical Report
• Qwen3-VL Technical Report
• Debiasing Large Vision-Language Models by Ablating Protected Attribute Representations
• InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
• MiniCPM-V: A GPT-4V Level MLLM on Your Phone
• The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs?
• Debiasing Multimodal Large Language Models via Penalization of Language Priors

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