Summary of Brand Visibility in Packaging: a Deep Learning Approach For Logo Detection, Saliency-map Prediction, and Logo Placement Analysis, by Alireza Hosseini et al.
Brand Visibility in Packaging: A Deep Learning Approach for Logo Detection, Saliency-Map Prediction, and Logo Placement Analysis
by Alireza Hosseini, Kiana Hooshanfar, Pouria Omrani, Reza Toosi, Ramin Toosi, Zahra Ebrahimian, Mohammad Ali Akhaee
First submitted to arxiv on: 4 Mar 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 The proposed framework measures the brand logo’s attention on a packaging design by leveraging YOLOv8 for precise logo detection across prominent datasets. The method consists of three steps: logo detection, modeling user visual attention with a novel saliency prediction model tailored for the packaging context, and integrating logo detection with a saliency map generation to provide a comprehensive brand attention score. The effectiveness is assessed module by module, ensuring thorough evaluation of each component. State-of-the-art models are compared, showing the superiority of the proposed methods. A unique dataset is collected to investigate previous psychophysical hypotheses related to brand visibility and introduce seven new hypotheses on the impact of position, orientation, presence of person, and other visual elements on brand attention. The research marks a significant stride in cognitive psychology, computer vision, and marketing, paving the way for advanced, consumer-centric packaging designs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how people see brand logos on product packaging. It’s like a recipe to measure what grabs our attention. First, it uses YOLOv8 to find the logo accurately. Then, it creates a special map of where our eyes focus (saliency). Finally, it combines these two maps to give us a score for how well the brand logo stands out. The results show that this method is better than others and helps us understand why some packaging designs are more effective. |
Keywords
» Artificial intelligence » Attention
