Summary of Verbalized Representation Learning For Interpretable Few-shot Generalization, by Cheng-fu Yang et al.
Verbalized Representation Learning for Interpretable Few-Shot Generalization
by Cheng-Fu Yang, Da Yin, Wenbo Hu, Nanyun Peng, Bolei Zhou, Kai-Wei Chang
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 Verbalized Representation Learning (VRL) approach is a novel method for extracting human-interpretable features from few-shot data, leveraging the Vision-Language Model (VLM) to identify key discriminative features between classes and shared characteristics within classes. This leads to feature vectors that can be used to train and infer with downstream classifiers. The experimental results show a 24% absolute improvement over prior state-of-the-art methods while using 95% less data and a smaller model, and the learned features exhibit a 20% absolute gain when used for downstream classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VRL is an innovative way to help computers recognize objects by automatically extracting important details from only a few examples. This can be super helpful in situations where we don’t have much data to work with. The approach uses special models that combine computer vision and language processing skills, allowing it to identify what makes different classes distinct while also finding commonalities within the same class. This leads to better object recognition and classification results. |
Keywords
» Artificial intelligence » Classification » Few shot » Language model » Representation learning
