Summary of Simplicity in Complexity : Explaining Visual Complexity Using Deep Segmentation Models, by Tingke Shen et al.
Simplicity in Complexity : Explaining Visual Complexity using Deep Segmentation Models
by Tingke Shen, Surabhi S Nath, Aenne Brielmann, Peter Dayan
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed paper develops a novel approach to modeling visual complexity, which plays a crucial role in various cognitive phenomena such as attention, engagement, and memorability. The current understanding of complexity is limited by the use of handcrafted features that are dataset-specific, failing to generalize across different image sets. Recent deep learning-based models have shown promise but lack interpretability and theoretical grounding. To bridge this gap, the authors employ state-of-the-art segmentation models, SAM and FC-CLIP, to quantify the number of segments at multiple granularities and the number of classes in an image. The results demonstrate that complexity can be effectively captured using a simple linear model incorporating these two features across six diverse image sets, including naturalistic scenes and artworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks into how images are complex or not. It’s like trying to figure out what makes a picture interesting or boring. Right now, there are many ways to measure this complexity, but they’re often specific to certain types of pictures and don’t work well for others. Some newer methods use special kinds of artificial intelligence called deep learning, but these models can be hard to understand and don’t give us much insight into why images are complex or not. To solve this problem, the authors came up with a new way to measure complexity using computer vision techniques that break down pictures into smaller parts. They tested their method on many different types of pictures and found that it worked really well. This could be important for understanding how we process information and make decisions. |
Keywords
» Artificial intelligence » Attention » Deep learning » Grounding » Sam
