DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain
Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain remains constrained by benchmarks that rely on coarse-grained categories, single-view imagery, and inaccurate metadata. To bridge this gap, we introduce DiningBench, a hierarchical, multi-view benchmark designed to evaluate VLMs across three levels of cognitive complexity: Fine-Grained Classification, Nutrition Estimation, and Visual Question Answering. Unlike previous datasets, DiningBench comprises 3,021 distinct dishes with an average of 5.27 images per entry, incorporating fine-grained "hard" negatives from identical menus and rigorous, verification-based nutritional data. We conduct an extensive evaluation of 29 state-of-the-art open-source and proprietary models. Our experiments reveal that while current VLMs excel at general reasoning, they struggle significantly with fine-grained visual discrimination and precise nutritional reasoning. Furthermore, we systematically investigate the impact of multi-view inputs and Chain-of-Thought reasoning, identifying five primary failure modes. DiningBench serves as a challenging testbed to drive the next generation of food-centric VLM research. All codes are released in https://github.com/meituan/DiningBench.
69dde0abaea31e227c22c65f
Song Jin
69dde0abaea31e227c22c660
Juntian Zhang
69dde0abaea31e227c22c661
Xun Zhang
69dde0abaea31e227c22c662
Zeying Tian
69dde0abaea31e227c22c663
Fei Jiang
69dde0abaea31e227c22c664
Guojun Yin
69dde0abaea31e227c22c665
Wei Lin
69dde0abaea31e227c22c666
Yong Liu
69dde0abaea31e227c22c667
Rui Yan
DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain
Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain remains constrained by benchmarks that rely on coarse-grained categories, single-view imagery, and inaccurate metadata. To bridge this gap, we introduce DiningBench, a hierarchical, multi-view benchmark designed to evaluate VLMs across three levels of cognitive complexity: Fine-Grained Classification, Nutrition Estimation, and Visual Question Answering. Unlike previous datasets, DiningBench comprises 3,021 distinct dishes with an average of 5.27 images per entry, incorporating fine-grained "hard" negatives from identical menus and rigorous, verification-based nutritional data. We conduct an extensive evaluation of 29 state-of-the-art open-source and proprietary models. Our experiments reveal that while current VLMs excel at general reasoning, they struggle significantly with fine-grained visual discrimination and precise nutritional reasoning. Furthermore, we systematically investigate the impact of multi-view inputs and Chain-of-Thought reasoning, identifying five primary failure modes. DiningBench serves as a challenging testbed to drive the next generation of food-centric VLM research. All codes are released in https://github.com/meituan/DiningBench.
A new hierarchical, multi-view benchmark called DiningBench is introduced to evaluate vision-language models on fine-grained food classification, nutrition estimation, and visual question answering, revealing current models' limitations in fine-grained visual discrimination and nutritional reasoning.
Vision-Language Models fine-grained classification nutrition estimation visual question answering multi-view imaging Chain-of-Thought reasoning