Summary of Low-rank Similarity Mining For Multimodal Dataset Distillation, by Yue Xu et al.
Low-Rank Similarity Mining for Multimodal Dataset Distillation
by Yue Xu, Zhilin Lin, Yusong Qiu, Cewu Lu, Yong-Lu Li
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposes a novel approach called Low-Rank Similarity Mining (LoRS) for distilling multimodal datasets, specifically image-text pairs. The authors argue that existing methods are not well-suited for this task due to the lack of inherent categorization in unimodal data. Instead, they suggest emphasizing modality correspondence and propose a method that concurrently distills a ground truth similarity matrix with image-text pairs, while leveraging low-rank factorization for efficiency and scalability. The proposed approach outperforms existing algorithms, marking a significant contribution to the field of visual-language dataset distillation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about finding ways to train machines to better understand the connection between images and text. Right now, it’s hard to get computers to learn from this kind of data because it’s very different from just looking at pictures or reading words separately. The authors suggest a new way to teach machines to recognize patterns in image-text pairs by using something called low-rank similarity mining. This approach is really good at finding the connections between images and text, which could be super helpful for things like image captioning or visual question answering. |
Keywords
» Artificial intelligence » Distillation » Image captioning » Question answering
