arxiv: 2604.10425 · v1 · submitted 2026-04-12 · 💻 cs.CV

Recognition: unknown

DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain

Song Jin , Juntian Zhang , Xun Zhang , Zeying Tian , Fei Jiang , Guojun Yin , Wei Lin , Yong Liu

show 1 more author

Rui Yan

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:59 UTC · model grok-4.3

classification 💻 cs.CV

keywords vision-language modelsfood recognitionnutritional estimationbenchmark datasetfine-grained classificationvisual question answeringmulti-view imagesdietary reasoning

0 comments

The pith

Vision-language models perform well on general reasoning but struggle with fine-grained food discrimination and precise nutritional estimation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents DiningBench to fill gaps in existing food benchmarks that use coarse categories, single views, and unreliable nutrition data. It evaluates 29 models on three task levels using 3021 dishes with multiple images per dish, hard negatives from the same menus, and verified nutrition facts. Experiments show models handle broad questions adequately yet fail at distinguishing visually similar dishes and calculating accurate nutrient values. Analysis of multi-view inputs and chain-of-thought prompting surfaces five recurring failure modes. The benchmark is positioned as a testbed to advance food-specific vision-language research.

Core claim

DiningBench is a hierarchical multi-view benchmark for dietary perception and reasoning that contains 3021 distinct dishes with an average of 5.27 images each, fine-grained hard negatives drawn from identical menus, and rigorously verified nutritional metadata. Large-scale testing of open-source and proprietary VLMs demonstrates strong general reasoning alongside clear deficits in fine-grained visual discrimination and exact nutrition calculation, with five primary failure modes identified.

What carries the argument

DiningBench, a three-level hierarchical benchmark (fine-grained classification, nutrition estimation, visual question answering) that supplies multi-view images and verified metadata to isolate visual and nutritional reasoning gaps.

If this is right

Multi-view image inputs improve performance on dietary tasks but leave substantial gaps in visual precision and nutrition accuracy.
Chain-of-thought prompting aids general reasoning yet fails to close the deficits in fine-grained discrimination and exact nutrient values.
The five identified failure modes point to specific needs for better fine-grained feature extraction in vision-language models.
DiningBench supplies a standardized, challenging evaluation set for measuring progress in food-centric VLM development.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The benchmark structure could be adapted to create similar hierarchical tests for other domains requiring fine visual detail and quantitative reasoning, such as medical imaging.
The documented gaps suggest that large-scale pretraining corpora contain insufficient examples of visually similar foods and precise nutrient associations.
Applications in health tracking or restaurant recommendation systems may need supplementary fine-tuning or specialized modules to reach reliable nutritional outputs.

Load-bearing premise

The verified nutritional metadata and hard-negative examples from identical menus provide a reliable test of genuine visual discrimination and nutritional reasoning that extends beyond the specific collected images.

What would settle it

A model achieving high accuracy on fine-grained dish classification and nutrition estimation tasks even with single-view inputs would show the reported performance gaps are not fundamental to current VLM architectures.

Figures

Figures reproduced from arXiv: 2604.10425 by Fei Jiang, Guojun Yin, Juntian Zhang, Rui Yan, Song Jin, Wei Lin, Xun Zhang, Yong Liu, Zeying Tian.

**Figure 2.** Figure 2: DiningBench Data Construction Pipeline. The process is divided into two phases: (1) Base Data [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Distribution of Nutritional Values. Histograms illustrating the frequency distribution of Calories (kcal), [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Impact of Multi-View Inputs. Performance trends for Classification (Accuracy) and Nutrition Estimation [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Impact of CoT on Nutrition Estimation. A [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Sample Case: Fine-Grained Classification. An [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 8.** Figure 8: Sample Case: Visual Question Answering. An [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

**Figure 9.** Figure 9: Impact of Chain-of-Thought on Classification. [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

DiningBench adds a multi-view hierarchical benchmark for food VLMs with some useful structure, but the nutrition verification and hard-negative details are too thin to fully back the failure-mode claims.

read the letter

DiningBench introduces a new test set of 3,021 dishes, each with an average of 5.27 images, organized into three levels: fine-grained classification, nutrition estimation, and visual question answering. It includes hard negatives drawn from identical menus and claims to use verified nutrition metadata. The authors ran 29 models and report that current VLMs manage general reasoning but fall short on precise visual discrimination and nutrition numbers. They also flag five failure modes and release the code on GitHub. This is the core contribution: a more granular, multi-view resource than the coarse single-image food datasets that came before. The hierarchical setup and same-menu negatives are reasonable ways to increase difficulty without inventing new tasks from scratch. Releasing code helps others check or extend the work. The evaluation covers both open and proprietary models, which gives a broad snapshot. The soft spots sit in the data construction. The abstract calls the nutrition data “rigorous, verification-based” and the negatives “fine-grained hard” ones, yet supplies no protocol for how nutrition facts were sourced or cross-checked, what inter-annotator agreement looked like, or how visual similarity was ensured when picking distractors from the same menu. If those steps have noise or rely on textual cues rather than appearance, the reported struggles with discrimination and nutrition could be inflated. The stress-test note flags exactly this gap, and nothing in the abstract resolves it. This paper is for researchers building or testing VLMs on health, nutrition, or restaurant applications. Readers who need a tougher benchmark in the dietary domain will find concrete tasks and baselines here. It deserves a serious referee because the overall framing and scale are solid enough to warrant review, even if the validation sections need expansion. I would send it to peer review with a request for the missing data-collection and quality-control details so the central claims can be assessed properly.

Referee Report

2 major / 2 minor

Summary. The paper introduces DiningBench, a hierarchical multi-view benchmark for VLMs in the dietary domain consisting of 3,021 dishes (avg. 5.27 images each) with fine-grained hard negatives drawn from identical menus and verification-based nutritional metadata. The benchmark spans three task levels—Fine-Grained Classification, Nutrition Estimation, and Visual Question Answering—and reports an evaluation of 29 open-source and proprietary VLMs. The central finding is that current VLMs perform well on general reasoning but struggle with fine-grained visual discrimination and precise nutritional reasoning; the authors also identify five primary failure modes and release code.

Significance. If the ground-truth construction is sound, DiningBench would be a valuable addition to the field by supplying a more challenging, multi-view, and nutritionally grounded testbed than existing coarse food datasets. The empirical evaluation of 29 models, systematic study of multi-view and CoT effects, and explicit failure-mode analysis provide concrete, falsifiable evidence of current VLM limitations in a real-world domain.

major comments (2)

[Benchmark construction] Benchmark construction section: the abstract and introduction assert 'rigorous, verification-based nutritional data' and 'fine-grained hard negatives from identical menus,' yet no protocol is supplied for nutritional metadata sources, expert review process, inter-annotator agreement, cross-check error rates, or how negatives were chosen to guarantee visual similarity while eliminating menu-level or textual cues. These omissions are load-bearing for the Nutrition Estimation and Fine-Grained Classification claims, because label noise or non-visual distractors could produce the reported performance gaps instead of model deficiencies.
[Experiments] Experiments section (evaluation of 29 models): the headline result that VLMs 'struggle significantly with fine-grained visual discrimination and precise nutritional reasoning' rests on the unelaborated premises above. Without the missing validation statistics, the quantitative gaps cannot be confidently attributed to the intended capabilities rather than data artifacts.

minor comments (2)

[Introduction] The five failure modes are mentioned in the abstract and conclusion but are not enumerated or illustrated with examples in the introduction; a brief list or forward reference would improve readability.
[Results] Table or figure captions for the model comparison results should explicitly state the number of images per dish used in multi-view experiments and any statistical significance tests applied to the reported accuracy differences.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful review and valuable feedback on our manuscript. The comments highlight important aspects of benchmark transparency that will improve the paper. We address each major comment below and will revise the manuscript accordingly to incorporate the requested details.

read point-by-point responses

Referee: [Benchmark construction] Benchmark construction section: the abstract and introduction assert 'rigorous, verification-based nutritional data' and 'fine-grained hard negatives from identical menus,' yet no protocol is supplied for nutritional metadata sources, expert review process, inter-annotator agreement, cross-check error rates, or how negatives were chosen to guarantee visual similarity while eliminating menu-level or textual cues. These omissions are load-bearing for the Nutrition Estimation and Fine-Grained Classification claims, because label noise or non-visual distractors could produce the reported performance gaps instead of model deficiencies.

Authors: We agree that a more explicit protocol is necessary to substantiate the claims of rigorous construction. The manuscript described the high-level approach and verification-based nature of the data but did not elaborate the full pipeline. In the revised version, we will expand the Benchmark Construction section with: (1) sources of nutritional metadata and the verification steps used; (2) the expert review process; (3) inter-annotator agreement metrics from the annotation and verification stages; (4) cross-check error rates observed; and (5) the criteria and process for selecting fine-grained hard negatives from identical menus, ensuring visual similarity while removing textual or menu-level cues. We will also add illustrative examples and a summary table of the verification outcomes in the appendix. These additions will allow readers to evaluate whether label quality supports the reported performance gaps. revision: yes
Referee: [Experiments] Experiments section (evaluation of 29 models): the headline result that VLMs 'struggle significantly with fine-grained visual discrimination and precise nutritional reasoning' rests on the unelaborated premises above. Without the missing validation statistics, the quantitative gaps cannot be confidently attributed to the intended capabilities rather than data artifacts.

Authors: We concur that the attribution of results to model limitations depends on demonstrating benchmark quality. The headline findings are presented under the premise of verified labels, which the expanded construction details will now document explicitly. In the revised Experiments section, we will add a dedicated paragraph referencing the new validation statistics and discussing how the construction process reduces the likelihood of artifacts driving the observed gaps. We will also note any remaining limitations in label certainty. This will provide the necessary grounding for interpreting the quantitative results as reflecting VLM deficiencies in fine-grained discrimination and nutritional reasoning. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark introduction and empirical evaluation are self-contained.

full rationale

The paper presents DiningBench as a new dataset with 3,021 dishes, multi-view images, hard negatives, and verified nutritional metadata, followed by direct evaluation of 29 VLMs on three task levels. No equations, parameter fitting, or derivation chain exists that could reduce results to inputs by construction. Claims about VLM performance gaps rest on the new benchmark's construction and testing rather than any self-definitional loop, fitted prediction, or load-bearing self-citation. The work is therefore self-contained against external benchmarks and does not trigger any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The benchmark rests on standard assumptions from computer vision and nutrition science rather than new free parameters or invented entities.

axioms (2)

domain assumption Vision-language models can be meaningfully evaluated on food-domain perception and reasoning using image-text pairs
Invoked throughout the benchmark design and model testing described in the abstract.
domain assumption Verified nutritional metadata provides ground truth for quantitative reasoning tasks
Used to support the nutrition estimation and VQA components.

pith-pipeline@v0.9.0 · 5528 in / 1357 out tokens · 47293 ms · 2026-05-10T15:59:50.117211+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

10 extracted references · 10 canonical work pages · 2 internal anchors

[1]

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Food-101–mining discriminative components with random forests. InEuropean conference on computer vision, pages 446–461. Springer. Jingjing Chen and Chong-Wah Ngo. 2016. Deep-based ingredient recognition for cooking recipe retrieval. InProceedings of the 24th ACM international con- ference on Multimedia, pages 32–41. Lin Chen, Jinsong Li, Xiaoyi Dong, Pan ...

work page internal anchor Pith review Pith/arXiv arXiv 2016
[2]

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Measuring multimodal mathematical reason- ing with math-vision dataset.Advances in Neural Information Processing Systems, 37:95095–95169. Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, and 1 others. 2025. In- ternvl3. 5: Advancing open-source multimodal mod- els in versatility,...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[3]

food_description: The merchant’s description. It may contain reference calorie info, but you must adhere to the following rules to obtain real, objective, and precise values: - Determine if the stated calories are for the whole dish or per 100g. If per 100g, calculate the total based onfood_unit. - If there are different specifications/sizes, use the data...

work page
[4]

Do not overlook decimal points (.) in numerical values

work page
[5]

When uncertain, cross-reference withfood_infoand description

work page
[6]

refined_dish_name

If calorie info exists in both picture and description, choose the source that is more compre- hensive. Food Metadata:food_info: <%s>. Output strictly according to the following JSON format defined in output_format. Do not output any other characters (no markdown, no explanations), ensure I can directly usejson.loads()! output_format = { "refined_dish_nam...

work page
[7]

Candidate Options: {option_str}

work page
[8]

Braised Beef Noodles

Standard Answer (Ground Truth): {gt_letter}. {correct_name} [Pass Criteria - Must meet all] 1.Image Quality: The image is clear with a distinct subject. 2.Correct GT: The standard answer must correctly describe the food in the image. 3.Unique Answer: Although distractors may look very similar (e.g., “Braised Beef Noodles” vs. “Spicy Beef Noodles”), they m...

work page
[9]

Strictly NO markdown code blocks

Output only a pure JSON object. Strictly NO markdown code blocks

work page
[10]

None": Data is perfect. -

Even if a minor flaw is found, judge as false. We need perfect data. [JSON Field Definitions] -is_valid(bool):trueonly implies the data is flawless; otherwisefalse. -error_type(str): Select the primary error type: -"None": Data is perfect. -"Bad_Image": Image quality issue. -"Data_Logic_Error": Nutrients and Total Calories are mathematically inconsistent....

work page