Summary of Losing Visual Needles in Image Haystacks: Vision Language Models Are Easily Distracted in Short and Long Contexts, by Aditya Sharma et al.
Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts
by Aditya Sharma, Michael Saxon, William Yang Wang
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper introduces LoCoVQA, a novel benchmark generator that challenges vision language models (VLMs) to reason about complex scenes by incorporating longer context sequences. The proposed framework, LoCoVQA, generates test examples for tasks such as mathematical reasoning, visual question answering (VQA), and character recognition by combining images from both in-distribution and out-of-distribution datasets. This allows VLMs to demonstrate their ability to comprehend and extract relevant information from larger contextual environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoCoVQA is a tool that helps test how well computer vision language models can understand and make sense of big pictures with lots of details. It makes the models do math problems, answer questions about what they see, and recognize characters in longer and more complex scenarios than usual. This makes it easier to compare how different models perform at this kind of task. |
Keywords
» Artificial intelligence » Question answering
