Summary of Can We Talk Models Into Seeing the World Differently?, by Paul Gavrikov et al.
Can We Talk Models Into Seeing the World Differently?
by Paul Gavrikov, Jovita Lukasik, Steffen Jung, Robert Geirhos, M. Jehanzeb Mirza, Margret Keuper, Janis Keuper
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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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 investigates the biases and preferences of vision language models (VLMs) when combining large language models (LLMs) with vision encoders. Unlike uni-modal models, VLMs exhibit biases inherited from their vision encoders, particularly in texture vs. shape recognition. The study finds that multi-modality has a direct impact on model behavior, altering how visual cues are processed. The authors demonstrate the potential to steer VLM outputs towards specific visual cues, but highlight limitations and variations depending on the type of classification sought. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at special kinds of computer models called vision language models. These models can understand both words and pictures! Researchers wondered if these models would keep some biases they learned from just looking at pictures or from understanding what we say. They found out that yes, some biases stick around, like recognizing shapes better than textures. This is different from how regular picture-recognizing computers work. The study also showed that by giving the model simple language instructions, we can influence what it recognizes, but sometimes this works better for certain types of recognition than others. |
Keywords
* Artificial intelligence * Classification
