Summary of Think Before You Act: a Two-stage Framework For Mitigating Gender Bias Towards Vision-language Tasks, by Yunqi Zhang et al.
Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks
by Yunqi Zhang, Songda Li, Chunyuan Deng, Luyi Wang, Hui Zhao
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses gender bias in Vision-Language Models (VLMs), which can perpetuate harmful stereotypes and discrimination. The authors identify object hallucination as the core issue, where VLMs focus on salient or familiar image attributes while neglecting contextualized nuances. They also note that most VLMs rely on co-occurrence between specific objects and gender attributes to infer ignored features, leading to gender bias. To mitigate this bias, the authors propose GAMA, a task-agnostic generation framework comprising two stages: narrative generation and answer inference. During narrative generation, GAMA produces all-sided but gender-obfuscated narratives, preventing premature concentration on localized image features. In the answer inference stage, GAMA integrates the image, generated narrative, and task-specific question prompt to infer answers for various vision-language tasks. The authors demonstrate GAMA’s debiasing and generalization capabilities through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that computer models don’t have a bias towards certain genders. Right now, these models can be quite mean-spirited by perpetuating harmful stereotypes. The problem lies in how the models look at images. They focus on what’s easy to see and ignore important details. This leads to them making incorrect assumptions about gender. To fix this, the researchers came up with a new way to make these models think more carefully about gender. They call it GAMA. It works by creating fake stories that don’t give away gender information, then using those stories to get answers for different tasks. The results show that GAMA is effective in reducing bias and can be used for many different tasks. |
Keywords
» Artificial intelligence » Generalization » Hallucination » Inference » Prompt
