Summary of Discriminative Fine-tuning Of Lvlms, by Yassine Ouali et al.
Discriminative Fine-tuning of LVLMs
by Yassine Ouali, Adrian Bulat, Alexandros Xenos, Anestis Zaganidis, Ioannis Maniadis Metaxas, Brais Martinez, Georgios Tzimiropoulos
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to vision-language representation learning is presented in this research paper. The study focuses on Contrastively-trained Vision-Language Models (VLMs), specifically CLIP, which have become the standard for discriminative vision-language modeling. However, these models are limited in their language understanding and often exhibit a “bag of words” behavior. To address this limitation, the authors propose Large Vision-Language Models (LVLMs) that combine vision encoders with Large Language Models (LLMs). The proposed LVLMs demonstrate improved detailed vision-language reasoning capabilities, but their autoregressive nature makes them less suitable for discriminative tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores new ways to understand and represent visual information using language. Currently, some computer models are very good at recognizing images and understanding text, but they struggle when combining both. The authors of this study create a new type of model that can do both better. They show that these new models can understand complex relationships between what we see and what we read. This could be useful for many applications, such as helping computers understand natural language or improving image recognition. |
Keywords
» Artificial intelligence » Autoregressive » Bag of words » Language understanding » Representation learning
