Summary of A Unified Debiasing Approach For Vision-language Models Across Modalities and Tasks, by Hoin Jung et al.
A Unified Debiasing Approach for Vision-Language Models across Modalities and Tasks
by Hoin Jung, Taeuk Jang, Xiaoqian Wang
First submitted to arxiv on: 10 Oct 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 The paper introduces Selective Feature Imputation for Debiasing (SFID), a novel methodology to reduce biases in Vision-Language Models (VLMs) without requiring extensive retraining. SFID integrates feature pruning and low confidence imputation (LCI) to maintain the semantic integrity of outputs while effectively reducing gender biases across various tasks, including zero-shot classification, text-to-image retrieval, image captioning, and text-to-image generation. This approach not only enhances fairness but also preserves efficiency and utility in diverse scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make artificial intelligence better by making sure it’s fair to everyone. Right now, some AI systems can be biased towards certain groups of people. This paper introduces a new way to fix this problem without needing to retrain the entire system. It works by removing certain features that are not important and filling in gaps where the AI is unsure. The results show that this approach makes AI more fair while still being good at its job. |
Keywords
» Artificial intelligence » Classification » Image captioning » Image generation » Pruning » Zero shot
