Summary of Enhancing Advanced Visual Reasoning Ability Of Large Language Models, by Zhiyuan Li et al.
Enhancing Advanced Visual Reasoning Ability of Large Language Models
by Zhiyuan Li, Dongnan Liu, Chaoyi Zhang, Heng Wang, Tengfei Xue, Weidong Cai
First submitted to arxiv on: 21 Sep 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 This paper proposes Complex Visual Reasoning Large Language Models (CVR-LLM), which combines the strengths of Vision-Language Models (VLMs) and Large Language Models (LLMs). The authors aim to bridge the gap between VLMs’ visual perception capabilities and LLMs’ text reasoning abilities. They achieve this by transforming images into detailed descriptions using an iterative self-refinement loop, leveraging LLMs’ text knowledge for predictions without extra training. Additionally, they introduce a novel multi-modal in-context learning (ICL) methodology to enhance LLMs’ contextual understanding and reasoning. The authors also present Chain-of-Comparison (CoC), a step-by-step comparison technique enabling contrasting various aspects of predictions. Their CVR-LLM achieves state-of-the-art performance across a wide range of complex visual reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can make computers better at understanding and using information from both pictures and words. It creates a new kind of computer model that combines the strengths of two different types of models: ones that are great at looking at pictures, and ones that are good at understanding written text. This new model is able to take in images and turn them into detailed descriptions, which it can then use to make predictions without needing extra training. The authors also come up with a new way for computers to learn and understand context better. They test their new model on many different tasks and find that it performs very well. |
Keywords
» Artificial intelligence » Multi modal
