This research paper introduces LOCOVQA, a benchmark generator designed to evaluate the long-context extractive reasoning capabilities of vision-language models (VLMs). The authors argue that existing VLMs struggle to identify relevant information when presented with multiple images, particularly as the number of distractor images increases.
Research Objective:
The study aims to assess the ability of VLMs to perform extractive reasoning over images, focusing on their capacity to identify and utilize only the pertinent visual information while disregarding irrelevant distractions.
Methodology:
The researchers developed LOCOVQA, a dynamic benchmark generator that augments existing image comprehension datasets with varying numbers of distractor images. They evaluated nine different VLMs, including both open-source and proprietary models, on LOCOVQA-generated benchmarks based on OK-VQA, MMStar, and MNIST datasets. The models' performance was measured by their accuracy in answering questions related to the target images within the context of increasing visual distractions.
Key Findings:
Main Conclusions:
The authors conclude that current VLMs lack the robust extractive reasoning capabilities necessary for real-world applications involving long visual contexts. They suggest that future VLM training should incorporate tasks requiring attention across multiple context images to improve their ability to filter out irrelevant visual information.
Significance:
This research highlights a critical weakness in current VLMs, emphasizing the need for improved training methodologies to enhance their long-context reasoning abilities. The findings have significant implications for the development of more robust and reliable VLMs capable of handling complex visual environments.
Limitations and Future Research:
The study primarily focused on three specific datasets, and further evaluation on a wider range of tasks is necessary to generalize the findings. Additionally, exploring alternative training strategies that explicitly address the challenges of visual extractive reasoning is crucial for advancing the capabilities of VLMs.
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by Aditya Sharm... في arxiv.org 10-07-2024
https://arxiv.org/pdf/2406.16851.pdfاستفسارات أعمق