Summary of Multiple Instance Verification, by Xin Xu et al.
Multiple Instance Verification
by Xin Xu, Eibe Frank, Geoffrey Holmes
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed paper explores multiple-instance verification, a problem setting where a query instance is verified against a bag of target instances with heterogeneous, unknown relevancy. The authors demonstrate that naive adaptations of attention-based multiple instance learning (MIL) methods and standard verification methods like Siamese neural networks are unsuitable for this setting. Instead, they introduce a new pooling approach named “cross-attention pooling” (CAP), which includes two novel attention functions to address the challenge of distinguishing between highly similar instances in a target bag. The authors conduct empirical studies on three different verification tasks and demonstrate that CAP outperforms adaptations of state-of-the-art MIL methods by substantial margins, in terms of both classification accuracy and quality of the explanations provided for the classifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to verify one thing against many things when those things are not all equal. Right now, we don’t have good ways to do this. The authors try different approaches but they don’t work well. Then, they come up with a new idea called “cross-attention pooling” which helps them identify the most important things in each group. They test their approach on three different tasks and show that it does much better than other methods. This is important because we need to be able to verify things correctly, especially in situations where some things are more similar than others. |
Keywords
* Artificial intelligence * Attention * Classification * Cross attention
