6 papers
Fine-Grained Food Image Understanding via Target-Aware Data Alignment
Jui-Feng Chi, Wei-Lun Chu, Bruce Coburn +2
Fine-grained food visual--semantic understanding requires models to capture subtle distinctions across ingredients, cooking methods, doneness, color, texture, and plate composition…
Inference-Time Mitigation of Adversarial Political Bias in Large Language Models
Tejaswi V. Panchagnula, Bruce Coburn, Bryce J. Dietrich +3
As Large Language Models (LLMs) become the mainstay for information retrieval and summarization tasks, ensuring that they are always non-partisan and invulnerable to political bias…
Open-KNEAD: Knowledge-grounded Nutrition Estimation via Agentic Decomposition
Bruce Coburn, Jingbo Yue, Jinge Ma +3
Multimodal Large Language Models (MLLMs) are increasingly used for dietary assessment from meal images, where retrieval-augmented grounding was shown to sharpen nutrition estimates…
DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images
Gautham Vinod, Siddeshwar Raghavan, Bruce Coburn +1
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.…
Food Image Generation on Multi-Noun Categories
Xinyue Pan, Yuhao Chen, Jiangpeng He +1
Generating realistic food images for categories with multiple nouns is surprisingly challenging. For instance, the prompt "egg noodle" may result in images that incorrectly contain…
Comprehensive Evaluation of Large Multimodal Models for Nutrition Analysis: A New Benchmark Enriched with Contextual Metadata
Bruce Coburn, Jiangpeng He, Megan E. Rollo +3
Large Multimodal Models (LMMs) are increasingly applied to meal images for nutrition analysis. However, existing work primarily evaluates proprietary models, such as GPT-4. This le…