Summary of Convqg: Contrastive Visual Question Generation with Multimodal Guidance, by Li Mi et al.
ConVQG: Contrastive Visual Question Generation with Multimodal Guidance
by Li Mi, Syrielle Montariol, Javiera Castillo-Navarro, Xianjie Dai, Antoine Bosselut, Devis Tuia
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 research proposes Contrastive Visual Question Generation (ConVQG), a novel method for generating visual questions that are grounded in both image and textual constraints. The goal is to generate focused questions that are highly relevant to the image content while leveraging external knowledge triplets or expected answers. ConVQG uses a dual contrastive objective to discriminate between questions generated using single-modality and multi-modality approaches, demonstrating state-of-the-art performance on knowledge-aware and standard VQG benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re an AI trying to understand a scene. You can ask yourself questions about what’s happening in the image, but it’s hard to come up with good questions that are both related to the picture and use outside knowledge. This paper introduces a new way to do this called Contrastive Visual Question Generation (ConVQG). It’s like asking questions while considering multiple angles or perspectives at once. |
