Summary of Uncertainty-aware Evaluation For Vision-language Models, by Vasily Kostumov et al.
Uncertainty-Aware Evaluation for Vision-Language Models
by Vasily Kostumov, Bulat Nutfullin, Oleg Pilipenko, Eugene Ilyushin
First submitted to arxiv on: 22 Feb 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 recent surge in popularity of Vision-Language Models like GPT-4, LLaVA, and CogVLM has been impressive, with their performance excelling in various vision-language tasks. However, current evaluation methods fail to account for an essential aspect: uncertainty quantification. This oversight is crucial for a comprehensive assessment of VLMs, as it allows for a more accurate understanding of model limitations and potential biases. To address this gap, we propose a benchmark that incorporates uncertainty quantification into evaluating VLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Vision-Language Models like GPT-4, LLaVA, and CogVLM are super smart computers that can do lots of cool things, like understand pictures and words together. But right now, the way we test these models is not very good because it doesn’t show how sure or unsure they are about what they’re doing. This is important to know because it helps us figure out where the models might be making mistakes or being unfair. To make our tests better, we created a new standard that shows how uncertain these models can be. |
Keywords
» Artificial intelligence » Gpt
