DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914455481221120 |
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| author | Vinod, Gautham Raghavan, Siddeshwar Coburn, Bruce Zhu, Fengqing |
| author_facet | Vinod, Gautham Raghavan, Siddeshwar Coburn, Bruce Zhu, Fengqing |
| contents | Accurate dietary assessment is critical for precision nutrition, yet most image-based methods rely on a single pre-consumption image and provide only coarse, meal-level estimates. These approaches cannot determine what was actually consumed and often require restrictive inputs such as depth sensing, multi-view imagery, or explicit segmentation. In this paper, we propose a simple vision-language framework for food-item-level nutritional analysis using paired before-and-after eating images. Instead of relying on rigid segmentation masks, our method leverages natural language prompts to localize specific food items and estimate their weight directly from a single RGB image. We further estimate food consumption by predicting weight differences between paired images using a two-stage training strategy. We evaluate our method on three publicly available datasets and demonstrate consistent improvements over existing approaches, establishing a strong baseline for before-and-after dietary image analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06352 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images Vinod, Gautham Raghavan, Siddeshwar Coburn, Bruce Zhu, Fengqing Computer Vision and Pattern Recognition Artificial Intelligence Multimedia Image and Video Processing Accurate dietary assessment is critical for precision nutrition, yet most image-based methods rely on a single pre-consumption image and provide only coarse, meal-level estimates. These approaches cannot determine what was actually consumed and often require restrictive inputs such as depth sensing, multi-view imagery, or explicit segmentation. In this paper, we propose a simple vision-language framework for food-item-level nutritional analysis using paired before-and-after eating images. Instead of relying on rigid segmentation masks, our method leverages natural language prompts to localize specific food items and estimate their weight directly from a single RGB image. We further estimate food consumption by predicting weight differences between paired images using a two-stage training strategy. We evaluate our method on three publicly available datasets and demonstrate consistent improvements over existing approaches, establishing a strong baseline for before-and-after dietary image analysis. |
| title | DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Multimedia Image and Video Processing |
| url | https://arxiv.org/abs/2604.06352 |