DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images
This addresses the need for more accurate dietary assessment in precision nutrition, though it appears incremental by building on existing before-and-after image approaches.
The paper tackled the problem of coarse dietary assessment from single images by proposing a vision-language framework that uses before-and-after eating images to estimate food-item-level nutritional analysis, demonstrating consistent improvements over existing methods on three datasets.
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.