FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis | Paper Citation Record

Observation 4d059550-1148-4db6-8d7f-dcb88df163ea · outbound

This paper cites Conversational Health Agents: A Personalized LLM-Powered Agent Framework.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Conversational Health Agents: A Personalized LLM-Powered Agent Framework

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:21:09.434199Z

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Observation 4aa23c38-c8b9-4f8e-906b-f29fb4a61aa8 · outbound

This paper cites Conversational health agents: a personalized large language model- powered agent framework.JAMIA open, 8(4):ooaf067.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Conversational health agents: a personalized large language model- powered agent framework.JAMIA open, 8(4):ooaf067

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.544829Z

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Observation e34badea-f558-4cc2-917c-0b425392889e · outbound

This paper cites Automatic food recognition us- ing deep convolutional neural networks with self-attention mechanism.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Automatic food recognition us- ing deep convolutional neural networks with self-attention mechanism

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.540086Z

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Observation 8c3dd937-f67c-4599-91d6-5e1dbe4268b8 · outbound

This paper cites Adaptllm/food-llama-3.2-11b-vision-instruct.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Adaptllm/food-llama-3.2-11b-vision-instruct

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.703662Z

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Observation 5fd3e12d-3719-4a7e-99d8-4fa759e0b39f · outbound

This paper cites A review on food recognition technology for health applications.Health psychology research, 8(3):9297.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis A review on food recognition technology for health applications.Health psychology research, 8(3):9297

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.713442Z

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Observation b00bc4c8-a80b-43fa-a34e-afc1f8c2e414 · outbound

This paper cites Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect.Expert Systems with Applications, 202:117362.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect.Expert Systems with Applications, 202:117362

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.694959Z

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Observation adb3f4b1-b9d0-4f10-b1d2-912a7c19e7b3 · outbound

This paper cites Twist & scout: Grounding multimodal llm-experts by forget-free tuning.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Twist & scout: Grounding multimodal llm-experts by forget-free tuning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.557299Z

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Observation afdfc6ef-7e66-4375-acfa-50f18296f370 · outbound

This paper cites Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:21:09.463741Z

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Observation 57c2eba4-fd20-4ae9-82eb-1289e22ab8a6 · outbound

This paper cites Adapting Large Language Models to Domains via Reading Comprehension.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Adapting Large Language Models to Domains via Reading Comprehension

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:21:09.426500Z

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Observation 97541e3c-b188-4661-9a23-645d1f3611af · outbound

This paper cites On domain- adaptive post-training for multimodal large language models.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis On domain- adaptive post-training for multimodal large language models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.699276Z

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Observation 26b8b751-9303-4f90-a933-ea6c4f1934b8 · outbound

This paper cites Food recognition for dietary assessment using deep convolutional neural networks.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Food recognition for dietary assessment using deep convolutional neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.553096Z

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Observation 769e627a-1637-4995-90bc-102d07664715 · outbound

This paper cites Intelligent agent for food recognition in a smart fridge.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Intelligent agent for food recognition in a smart fridge

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.549179Z

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Observation 687956c3-8b9b-46b7-9a64-3ab8b56019e4 · outbound

This paper cites Human visual system vs convolution neural networks in food recognition task: An empirical comparison.Computer Vision and Image Understanding, 191:102878.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Human visual system vs convolution neural networks in food recognition task: An empirical comparison.Computer Vision and Image Understanding, 191:102878

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.690309Z

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Observation 4383e44d-df3a-4458-8572-59eb26e11633 · outbound

This paper cites Improving Food Image Recognition with Noisy Vision Transformer.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Improving Food Image Recognition with Noisy Vision Transformer

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:21:09.452276Z

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Observation 6d29f26a-37ce-428f-b70b-ced1148437fc · outbound

This paper cites An integrated lightweight neural network design and fpga-accelerated edge computing for chili pepper variety and origin identification via an e-nose.Foods, 14(15):2612.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis An integrated lightweight neural network design and fpga-accelerated edge computing for chili pepper variety and origin identification via an e-nose.Foods, 14(15):2612

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.708895Z

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Observation d6ab3ea9-3728-46eb-b93d-dcfa6fd8aa65 · outbound

This paper cites Squeeze-and-excitation networks.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Squeeze-and-excitation networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.565988Z

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Observation 52fde040-4b2f-4cf8-b6fc-dbe4e8708682 · outbound

This paper cites Enhancing food recognition accuracy using hybrid transformer models and image preprocessing techniques.Scientific Reports, 15(1):5591.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Enhancing food recognition accuracy using hybrid transformer models and image preprocessing techniques.Scientific Reports, 15(1):5591

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.561796Z

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Observation 67b9d070-bb57-4cc1-b760-eafb37ba6b37 · outbound

This paper cites Food detection and recognition using convolutional neural network.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Food detection and recognition using convolutional neural network

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.677088Z

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Observation f9cf7166-1f8f-4a74-8c3a-d563043799dc · outbound

This paper cites Fine-grained food image classification and recipe extraction using a customized deep neural network and nlp.Computers in Biology and Medicine, 175:108528.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Fine-grained food image classification and recipe extraction using a customized deep neural network and nlp.Computers in Biology and Medicine, 175:108528

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.597618Z

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Observation 5cf7c847-8532-4812-b295-7e1d863742b2 · outbound

This paper cites A cloud edge collaboration of food recognition using deep neural networks.Journal of Artificial Intelligence and Computing, 2(1):9–18.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis A cloud edge collaboration of food recognition using deep neural networks.Journal of Artificial Intelligence and Computing, 2(1):9–18

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.588197Z

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Observation 0bc6bec6-355a-45c0-b3c9-e9dc1991a546 · outbound

This paper cites Deep learning approaches in food recognition.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Deep learning approaches in food recognition

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.672920Z

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Observation 3e03f335-2dd8-4743-b379-3331281427ce · outbound

This paper cites Corre- lation verification for image retrieval.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Corre- lation verification for image retrieval

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.681351Z

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Observation 0e043359-7b35-4c85-b811-1538a58c95d3 · outbound

This paper cites VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusion

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:21:09.469192Z

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Observation 964a129b-ca16-491f-845e-c0ecf926ce4a · outbound

This paper cites Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.583752Z

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Observation 2d793bf3-4ec9-4ac1-9cb5-aee046f5012c · outbound

This paper cites A new deep learning- based food recognition system for dietary assessment on an edge com- puting service infrastructure.IEEE Transactions on Services Computing, 11(2):249–261.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis A new deep learning- based food recognition system for dietary assessment on an edge com- puting service infrastructure.IEEE Transactions on Services Computing, 11(2):249–261

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.579388Z

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Observation 7db75c6b-4ded-4f50-b2be-9178425f6831 · outbound

This paper cites Food-500 cap: A fine-grained food caption benchmark for evaluating vision-language models.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Food-500 cap: A fine-grained food caption benchmark for evaluating vision-language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.659369Z

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Observation 92ad7b66-c339-4932-842b-b93ad9e6a4ab · outbound

This paper cites An explorative analysis of svm classifier and resnet50 architecture on african food classification.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis An explorative analysis of svm classifier and resnet50 architecture on african food classification

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.570511Z

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Observation 4085034f-ac96-4e82-b2d8-5b3323995aaf · outbound

This paper cites Nutrinet: a deep learn- ing food and drink image recognition system for dietary assessment.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Nutrinet: a deep learn- ing food and drink image recognition system for dietary assessment

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.574797Z

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Observation 3e8d673f-9205-4463-8598-46467f2a0101 · outbound

This paper cites Large scale visual food recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8):9932–9949.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Large scale visual food recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(8):9932–9949

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.655274Z

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Observation 04bcc795-6557-4ec9-9931-cdd8fc9659e1 · outbound

This paper cites The food recognition benchmark: Using deep learning to recognize food in images.Frontiers in Nutrition, 9:875143.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis The food recognition benchmark: Using deep learning to recognize food in images.Frontiers in Nutrition, 9:875143

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.667455Z

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Observation 15ca6a27-955c-4d00-aca5-9019ebbd3361 · outbound

This paper cites A novel hierarchical edge computing solution based on deep learning for distributed image recognition in iot systems.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis A novel hierarchical edge computing solution based on deep learning for distributed image recognition in iot systems

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.685689Z

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Observation bc28b8bd-c12d-4170-b93d-49338ec35f24 · outbound

This paper cites Opengvlab/internvl3-8b.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Opengvlab/internvl3-8b

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.717669Z

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Observation be39ce1a-ed55-47cc-80fb-b9597c64f6f5 · outbound

This paper cites Mobile multi-food recognition using deep learning.ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 13(3s):1–21.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Mobile multi-food recognition using deep learning.ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 13(3s):1–21

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.722045Z

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Observation 04e4d99d-7886-49fa-ad4a-b679c37dde00 · outbound

This paper cites A novel svm based food recognition method for calorie measurement applications.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis A novel svm based food recognition method for calorie measurement applications

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.726651Z

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Observation 97ed6138-5324-451a-9d39-556172ad81a0 · outbound

This paper cites Are vision-language models ready for dietary assessment? exploring the next frontier in ai-powered food image recognition.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Are vision-language models ready for dietary assessment? exploring the next frontier in ai-powered food image recognition

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.639613Z

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Observation fe33bdcf-6f83-4ac4-a582-fac86e281fb9 · outbound

This paper cites Foodai: Food image recognition via deep learning for smart food logging.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Foodai: Food image recognition via deep learning for smart food logging

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.592738Z

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Observation ef71b4ad-acdc-4712-a25d-093c9adeda68 · outbound

This paper cites Study for food recognition system using deep learning.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Study for food recognition system using deep learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.650899Z

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Observation 16e40c59-6eb5-4944-b7d6-9f366478d392 · outbound

This paper cites The role of artificial intelligence in nutrition research: a scoping review.Nutrients, 16(13):2066.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis The role of artificial intelligence in nutrition research: a scoping review.Nutrients, 16(13):2066

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.630815Z

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Observation f2d7356b-39b1-4ac7-9c71-259738e234b0 · outbound

This paper cites Qwen2.5-vl technical report.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Qwen2.5-vl technical report

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.634982Z

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Observation 8f53485a-58c4-4569-8755-cac005edc956 · outbound

This paper cites Perspectives of dietary assessment in human health and disease.Nutrients, 14(4):830.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Perspectives of dietary assessment in human health and disease.Nutrients, 14(4):830

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.644011Z

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Observation 933933de-d8f4-45b8-a68f-e3b79d597ca4 · outbound

This paper cites vikhyatk/moondream2.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis vikhyatk/moondream2

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.618146Z

Source-reported events for the cited work

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Observation 1ab275ea-b345-45d8-9eed-1679589d01f4 · outbound

This paper cites Foodsage: Addressing recognition uncertainty in automated dietary monitoring through human-robot dialogue.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Foodsage: Addressing recognition uncertainty in automated dietary monitoring through human-robot dialogue

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.622508Z

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Observation 7e830f57-aa8c-4ea4-aec1-1b594a6e5fa4 · outbound

This paper cites A closed-loop multi-agent system driven by llms for meal-level personalized nutrition management.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis A closed-loop multi-agent system driven by llms for meal-level personalized nutrition management

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:21:09.444889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 81977d2c-36cd-4a38-8bc9-430d9676b9c8 · outbound

This paper cites Food recognition and dietary assessment for healthcare system at mobile device end using mask r-cnn.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Food recognition and dietary assessment for healthcare system at mobile device end using mask r-cnn

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.614473Z

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Observation 8fa3bbec-98f6-4557-8e2c-0f06007efdf3 · outbound

This paper cites Foodlmm: A versatile food assistant using large multi- modal model.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Foodlmm: A versatile food assistant using large multi- modal model

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.626687Z

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Observation ab20b671-cb74-4ae5-a264-c1007591f0a1 · outbound

This paper cites Deep learning in food category recognition.Information Fusion, 98:101859.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Deep learning in food category recognition.Information Fusion, 98:101859

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.605889Z

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Observation f77e7de0-258b-420f-b718-dd86b226e6ea · outbound

This paper cites Foodsky: A food- oriented large language model that can pass the chef and dietetic examinations.Patterns, 6(5):101234.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Foodsky: A food- oriented large language model that can pass the chef and dietetic examinations.Patterns, 6(5):101234

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.610454Z

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Observation f9e673f9-1ef0-4b53-9c8b-4deabd83f477 · outbound

This paper cites Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models.

FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T10:32:12.601899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers