Summary of Bi-mdrg: Bridging Image History in Multimodal Dialogue Response Generation, by Hee Suk Yoon et al.
BI-MDRG: Bridging Image History in Multimodal Dialogue Response Generation
by Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Kang Zhang, Yu-Jung Heo, Du-Seong Chang, Chang D. Yoo
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM)
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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 propose a novel approach to Multimodal Dialogue Response Generation (MDRG), where models generate responses that can be text, images, or a blend of both. To overcome the limitations of previous work, which relies on an intermediary text modality for image inputs and outputs, the authors introduce BI-MDRG, which utilizes image history information for enhanced relevance and consistency in sequential responses. The proposed method is evaluated using the multimodal dialogue benchmark dataset, demonstrating improved quality and consistency in multimodal dialogue. Additionally, a new curated dataset of 300 dialogues is introduced to track object consistency across conversations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way to make computers better at understanding and responding to both text and images when they’re talking. Right now, computer programs can only respond with one type of information, but humans often use a mix of words and pictures to communicate. The researchers created a new approach called BI-MDRG that helps computers understand the context of an image and create more relevant and consistent responses. They tested this method using a big dataset and showed that it works better than previous approaches. |
