Summary of Visual Agents As Fast and Slow Thinkers, by Guangyan Sun et al.
Visual Agents as Fast and Slow Thinkers
by Guangyan Sun, Mingyu Jin, Zhenting Wang, Cheng-Long Wang, Siqi Ma, Qifan Wang, Tong Geng, Ying Nian Wu, Yongfeng Zhang, Dongfang Liu
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 abstract introduces FaST, a novel approach that incorporates the Fast and Slow Thinking mechanism into visual agents to address challenges in transitioning from structured benchmarks to real-world scenarios. It discusses how contemporary AI systems, driven by large language models, demonstrate human-like traits but fall short of genuine cognition. The paper presents FaST as a flexible system with hierarchical reasoning capabilities and transparent decision-making pipeline, which enables it to emulate human-like cognitive processes in visual intelligence. Empirical results demonstrate that FaST outperforms various well-known baselines on tasks such as visual question answering and reasoning segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FaST is an innovative approach to creating more human-like AI systems. Right now, AI can do many things that humans can, but it doesn’t really think like us. To fix this, the researchers created FaST, a system that can switch between two different thinking modes – one for quick decisions and one for careful consideration. This helps FaST make better choices when faced with new or uncertain situations. The results show that FaST does a great job on tasks like answering questions about pictures and segmenting objects in images. |
Keywords
* Artificial intelligence * Question answering
