Summary of Individuation in Neural Models with and Without Visual Grounding, by Alexey Tikhonov et al.
Individuation in Neural Models with and without Visual Grounding
by Alexey Tikhonov, Lisa Bylinina, Ivan P. Yamshchikov
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 research explores the encoding of individuation information by language-and-vision model CLIP compared to two text-only models, FastText and SBERT. The study examines latent representations provided by CLIP for substrates, granular aggregates, and various numbers of objects. Results show that CLIP embeddings capture quantitative differences in individuation more effectively than text-only trained models. Furthermore, the individuation hierarchy deduced from CLIP embeddings aligns with hierarchies proposed in linguistics and cognitive science. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research compares how a language-and-vision model called CLIP handles information about individual things compared to two other models that only use text. The study looks at what these models can learn from pictures of different objects and groups. The results show that the language-and-vision model is better at understanding differences between individual things. This is important because it helps us understand how our brains work when we think about individual things. |
Keywords
» Artificial intelligence » Fasttext
