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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.13950 |
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| _version_ | 1866909465083641856 |
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| author | Chappa, Naga VS Raviteja Shepard, Matthew McCurtain, Connor McCormick, Charlotte Dobbs, Page Daniel Luu, Khoa |
| author_facet | Chappa, Naga VS Raviteja Shepard, Matthew McCurtain, Connor McCormick, Charlotte Dobbs, Page Daniel Luu, Khoa |
| contents | While tobacco advertising innovates at unprecedented speed, traditional surveillance methods remain frozen in time, especially in the context of social media. The lack of large-scale, comprehensive datasets and sophisticated monitoring systems has created a widening gap between industry advancement and public health oversight. This paper addresses this critical challenge by introducing Tobacco-1M, a comprehensive dataset of one million tobacco product images with hierarchical labels spanning 75 product categories, and DEFEND, a novel foundation model for tobacco product understanding. Our approach integrates a Feature Enhancement Module for rich multimodal representation learning, a Local-Global Visual Coherence mechanism for detailed feature discrimination, and an Enhanced Image-Text Alignment strategy for precise product characterization. Experimental results demonstrate DEFEND's superior performance, achieving 83.1% accuracy in product classification and 73.8% in visual question-answering tasks, outperforming existing methods by significant margins. Moreover, the model exhibits robust zero-shot learning capabilities with 45.6% accuracy on novel product categories. This work provides regulatory bodies and public health researchers with powerful tools for monitoring emerging tobacco products and marketing strategies, potentially revolutionizing approaches to tobacco control and public health surveillance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13950 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Chappa, Naga VS Raviteja Shepard, Matthew McCurtain, Connor McCormick, Charlotte Dobbs, Page Daniel Luu, Khoa Computer Vision and Pattern Recognition While tobacco advertising innovates at unprecedented speed, traditional surveillance methods remain frozen in time, especially in the context of social media. The lack of large-scale, comprehensive datasets and sophisticated monitoring systems has created a widening gap between industry advancement and public health oversight. This paper addresses this critical challenge by introducing Tobacco-1M, a comprehensive dataset of one million tobacco product images with hierarchical labels spanning 75 product categories, and DEFEND, a novel foundation model for tobacco product understanding. Our approach integrates a Feature Enhancement Module for rich multimodal representation learning, a Local-Global Visual Coherence mechanism for detailed feature discrimination, and an Enhanced Image-Text Alignment strategy for precise product characterization. Experimental results demonstrate DEFEND's superior performance, achieving 83.1% accuracy in product classification and 73.8% in visual question-answering tasks, outperforming existing methods by significant margins. Moreover, the model exhibits robust zero-shot learning capabilities with 45.6% accuracy on novel product categories. This work provides regulatory bodies and public health researchers with powerful tools for monitoring emerging tobacco products and marketing strategies, potentially revolutionizing approaches to tobacco control and public health surveillance. |
| title | DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.13950 |