Summary of Why Context Matters in Vqa and Reasoning: Semantic Interventions For Vlm Input Modalities, by Kenza Amara et al.
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities
by Kenza Amara, Lukas Klein, Carsten Lüth, Paul Jäger, Hendrik Strobelt, Mennatallah El-Assady
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 The proposed research investigates how different modalities in Visual Language Models (VLMs) influence their performance and behavior in visual question answering (VQA) and reasoning tasks. The study focuses on the interplay between image and text modalities, measuring their effect through answer accuracy, reasoning quality, model uncertainty, and modality relevance. The research contributes a new dataset, SI-VQA, and benchmarks various VLM architectures under different modality configurations. The results show that complementary information between modalities improves performance, while contradictory information harms it. Image-text annotations have minimal impact on accuracy and uncertainty, but increase image relevance slightly. Attention analysis confirms the dominant role of image inputs over text in VQA tasks. State-of-the-art VLMs are evaluated, revealing PaliGemma’s harmful overconfidence and LLaVA models’ more robust performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how Visual Language Models (VLMs) use information from images and text to answer questions and solve problems. Researchers tested different ways of combining image and text information and found that when the two types of information work together well, VLMs do better. When they don’t work together well, it makes things worse. The researchers also looked at how much attention VLMs pay to images versus text and found that images are more important for solving some problems. |
Keywords
» Artificial intelligence » Attention » Question answering
