Summary of Embracing Diversity: Interpretable Zero-shot Classification Beyond One Vector Per Class, by Mazda Moayeri et al.
Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class
by Mazda Moayeri, Michael Rabbat, Mark Ibrahim, Diane Bouchacourt
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 new approach to open-world object classification using vision-language models (VLMs). The existing VLM classifiers perform well when objects are depicted similarly but struggle with diverse representations of the same class. To address this, the authors suggest encoding and accounting for intra-class diversity by inferring attributes in a zero-shot setting without retraining. The proposed method outperforms standard VLM classification on various datasets showcasing hierarchical structures, diverse object states, geographic variations, and fine-grained features. Notably, the approach provides interpretable explanations for each inference, facilitating model debugging and transparency. The authors also explore the trade-off between overall and worst-class accuracy, which can be tuned via a hyperparameter. This work aims to encourage further research into capturing diversity in open-world classification using zero-shot VLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how computers recognize objects. Right now, they’re not very good at recognizing things that look different from what they’ve seen before. The authors are proposing a new way to make these computers better. They want to teach the computer to understand that an object can look different in various ways and still be the same thing. For example, a pear can be whole or cut up, but it’s still a pear. This new approach works even when the computer hasn’t seen those different forms before. It also helps the computer explain why it made certain decisions, making it more transparent. The authors hope this work will inspire others to keep improving how computers recognize objects. |
Keywords
* Artificial intelligence * Classification * Hyperparameter * Inference * Zero shot
