Summary of Open-ended Vqa Benchmarking Of Vision-language Models by Exploiting Classification Datasets and Their Semantic Hierarchy, By Simon Ging et al.
Open-ended VQA benchmarking of Vision-Language models by exploiting Classification datasets and their semantic hierarchy
by Simon Ging, María A. Bravo, Thomas Brox
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research aims to improve our understanding of text-generative vision-language models by proposing new evaluation methodologies and a novel Visual Question Answering (VQA) benchmark. The benchmark is based on well-known visual classification datasets, allowing for a granular evaluation of these models and their comparison with discriminative vision-language models. The study also suggests using the semantic hierarchy of the label space to ask automatically generated follow-up questions about the ground-truth category. Additionally, the paper compares traditional NLP and LLM-based metrics for evaluating model predictions given ground-truth answers. A human evaluation study is performed to inform the decision on the final metric, and the benchmark is applied to a suite of vision-language models to demonstrate their abilities in object, action, and attribute classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how well text-generative vision-language models can answer questions about pictures. Right now, it’s hard to compare these models because they are not tested using the same criteria. The researchers propose a new way to test these models that is more fair and accurate. They also suggest asking follow-up questions based on the category of the object in the picture. Additionally, they compared different ways to measure how well a model does when given the correct answer. The results showed which models are better at answering certain types of questions. |
Keywords
* Artificial intelligence * Classification * Nlp * Question answering
