Summary of From Uncertainty to Trust: Enhancing Reliability in Vision-language Models with Uncertainty-guided Dropout Decoding, by Yixiong Fang et al.
From Uncertainty to Trust: Enhancing Reliability in Vision-Language Models with Uncertainty-Guided Dropout Decoding
by Yixiong Fang, Ziran Yang, Zhaorun Chen, Zhuokai Zhao, Jiawei Zhou
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposes a novel inference-time approach called Dropout Decoding to mitigate the challenges of large vision-language models (LVLMs) misinterpreting visual inputs. The method, inspired by dropout regularization, selectively masks uncertain visual tokens during decoding based on their uncertainty, measured by projecting them onto the text space and decomposing into aleatoric and epistemic components. By aggregating predictions from an ensemble of masked decoding contexts, Dropout Decoding robustly reduces object hallucinations (OH) and enhances reliability and quality of LVLM outputs across diverse visual contexts. The authors evaluate their approach on benchmarks including CHAIR, THRONE, and MMBench, demonstrating significant improvements in reducing OH and enhancing the overall performance of LVLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a way to make big AI models that look at pictures more accurate. These models are good at doing tasks like recognizing objects in images, but sometimes they get confused and make mistakes. The new method, called Dropout Decoding, helps fix this by looking at the uncertainty of each pixel in the image and hiding any pixels that are unsure or incorrect. This makes the model’s predictions more reliable and less likely to be wrong. The authors tested their approach on some big datasets and found that it really works! |
Keywords
» Artificial intelligence » Dropout » Inference » Regularization
