Summary of Lova3: Learning to Visual Question Answering, Asking and Assessment, by Henry Hengyuan Zhao et al.
LOVA3: Learning to Visual Question Answering, Asking and Assessment
by Henry Hengyuan Zhao, Pan Zhou, Difei Gao, Zechen Bai, Mike Zheng Shou
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 In this paper, researchers aim to enhance the capabilities of Multimodal Large Language Models (MLLMs) by introducing a framework called LOVA3 that enables MLLMs to answer, ask, and assess questions. The current state-of-the-art MLLMs primarily focus on question answering, neglecting the importance of questioning and assessment skills. To address this limitation, the authors introduce two supplementary training tasks: GenQA and EvalQA. These tasks are designed to foster the skills of asking and assessing questions in the context of images. The framework also includes a set of multimodal foundational tasks for developing questioning abilities. Additionally, the authors propose a new benchmark called EvalQABench, which consists of 64,000 training samples and 5,000 validation and testing samples. The paper evaluates the performance gains achieved by MLLMs trained using the LOVA3 framework on various multimodal datasets and benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers smarter by letting them ask and answer questions like humans do. Right now, computers can only answer simple questions, but they can’t really think or learn from what they find out. The researchers are trying to change this by teaching computers how to ask better questions and figure out if the answers they get are right or not. They’re doing this by creating special training tasks that help computers practice asking and answering questions about pictures. The goal is to make computers more intelligent and able to understand and learn from complex information. |
Keywords
» Artificial intelligence » Question answering
