Summary of Algorithm Research Of Elmo Word Embedding and Deep Learning Multimodal Transformer in Image Description, by Xiaohan Cheng et al.
Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description
by Xiaohan Cheng, Taiyuan Mei, Yun Zi, Qi Wang, Zijun Gao, Haowei Yang
First submitted to arxiv on: 26 Jul 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 The paper proposes an innovative approach to zero-sample learning that addresses overfitting issues in existing methods. By incorporating unknown classes with similar semantics into the vector space during construction, the model can better generalize to unseen classes. This method leverages category semantic similarity measures and is particularly effective for medical image analysis tasks. The authors also introduce a self-attention mechanism to extract visual features related to the original image, enhancing the model’s ability to learn from medical images. Experiments on three zero-shot learning datasets demonstrate the efficacy of this approach, achieving state-of-the-art results compared to advanced algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to teach machines without showing them many examples. This is helpful when we don’t have enough data or when there are unknown categories. The method uses words and meanings to group similar things together. It also looks at medical images in a special way, focusing on important parts. By doing this, the machine can learn better from what it sees. The authors tested their approach on three sets of data and found that it worked very well compared to other methods. |
Keywords
» Artificial intelligence » Overfitting » Self attention » Semantics » Vector space » Zero shot
