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Paper Citation Record · LEDGER

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights

As of 8 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2507.04412.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.04412 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:53:30.508953Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

65 of 65 outbound references displayed

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  • verified fuzzy50
  • unresolved10
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd67c55a-805a-4941-b87b-1e6310ee3be4 · outbound

This paper cites Fruitq: a new dataset of multiple fruit images for freshness evaluation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Fruitq: a new dataset of multiple fruit images for freshness evaluation

Reference 1

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Observation 21d3c249-8a86-417e-ad86-1fbccc82679b · outbound

This paper cites Live to eat and eat to live longer.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Live to eat and eat to live longer

Reference 2

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Observation f0ad4be2-2bd7-4f7b-a3f4-addacd6dfd6d · outbound

This paper cites Products-10K: A Large-scale Product Recognition Dataset.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Products-10K: A Large-scale Product Recognition Dataset

Reference 3

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Observation b606cedb-a9de-4038-87dc-9d6ad317d717 · outbound

This paper cites Recipenlg: A cooking recipes dataset for semi-structured text generation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Recipenlg: A cooking recipes dataset for semi-structured text generation

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation a02941e6-233f-4f96-9d14-5db765c7ce2e · outbound

This paper cites Food-101 – mining discriminative components with random forests.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Food-101 – mining discriminative components with random forests

Reference 5

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Observation 1192db75-84ba-49b4-a419-65b5d74653b5 · outbound

This paper cites Food-101–mining discriminative components with random forests.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Food-101–mining discriminative components with random forests

Reference 6

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Source-reported events for the cited work

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Observation 915d8674-0fc3-4836-a6a6-1e82ee673fc2 · outbound

This paper cites Bs-nets: An end- to-end framework for band selection of hyperspectral image.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Bs-nets: An end- to-end framework for band selection of hyperspectral image

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 6e1863e3-b0c1-4618-8633-c68fd1a15e96 · outbound

This paper cites Cascade r-cnn: High quality object detection and instance segmentation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Cascade r-cnn: High quality object detection and instance segmentation

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation cf6bb4c1-e44a-4f63-857c-2df9893b8e1b · outbound

This paper cites Deep-based ingredi- ent recognition for cooking recipe retrieval.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Deep-based ingredi- ent recognition for cooking recipe retrieval

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation ac2b869c-e48e-4288-9850-efa720ac09c7 · outbound

This paper cites Hybrid task cascade for instance seg- mentation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Hybrid task cascade for instance seg- mentation

Reference 10

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Observation e15b7d9e-d6f8-43db-88f4-45affa915a1e · outbound

This paper cites Beverage products packaging dataset for auto- matic shelf recognition and its application.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Beverage products packaging dataset for auto- matic shelf recognition and its application

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation f91378ed-08d4-46dd-b6b7-4017f8f3c1b0 · outbound

This paper cites Fire: Food image to recipe generation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Fire: Food image to recipe generation

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation ac3c4f76-364e-4167-a5b0-31cd822b9dcb · outbound

This paper cites A low-shot object counting network with iterative prototype adaptation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights A low-shot object counting network with iterative prototype adaptation

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c83bc18c-0b7f-4459-994d-a65ce41fe839 · outbound

This paper cites Retrieval and classi- fication of food images.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Retrieval and classi- fication of food images

Reference 14

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raw_fallback, observed 2026-08-06T19:53:40.946980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:24.894690Z digest=sha256:5b928f733cccd253eb763305c9d2bd30ccf85cdac5a627959ff21102fe067bf6

Observation 77230496-02f0-4345-bf9a-7cba348edf55 · outbound

This paper cites MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results

Reference 15

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Observation 53788b3a-0429-4150-8279-474062e8303a · outbound

This paper cites Mask r-cnn.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Mask r-cnn

Reference 16

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation f4a36621-0aee-4fa1-bd05-7e9709d0cd52 · outbound

This paper cites Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision

Reference 17

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Unavailable: canonical work link unavailable.

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Observation af555b54-8fda-49e5-8dd6-7fa72c130401 · outbound

This paper cites Vegfru: A domain-specific dataset for fine-grained visual categoriza- tion.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Vegfru: A domain-specific dataset for fine-grained visual categoriza- tion

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation a210059f-a7bd-40fc-9ded-6fdf8970425f · outbound

This paper cites One-shot neu- ral band selection for spectral recovery.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights One-shot neu- ral band selection for spectral recovery

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 54d6d49d-4b18-4f0b-b0a8-40277d67cd46 · outbound

This paper cites Mask scoring r-cnn.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Mask scoring r-cnn

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 8af477e4-f0b9-4ebf-b8b0-a88bbf533ab0 · outbound

This paper cites CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e3d4a9e6-7666-432a-acbf-c3d9f41cf6c9 · outbound

This paper cites Visible imaging to convolutionally discern and authenticate varieties of rice and their derived flours.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Visible imaging to convolutionally discern and authenticate varieties of rice and their derived flours

Reference 22

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raw_fallback, observed 2026-08-06T19:53:39.736409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:25.762549Z digest=sha256:fdf9d26604538e4096cf095c273584e7c1db7fc719617a6f21c5fb972da95c2f

Observation 74b033b2-88f1-465f-b6e8-e1c5920645c9 · outbound

This paper cites RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models

Reference 23

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Unavailable: canonical work link unavailable.

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Observation 2df4da36-6feb-4261-bb96-8608bf476906 · outbound

This paper cites FoodX-251: A Dataset for Fine-grained Food Classification.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights FoodX-251: A Dataset for Fine-grained Food Classification

Reference 24

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local_arxiv, observed 2026-08-06T19:53:31.379668Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e82665b2-9998-4311-bc46-725cee684806 · outbound

This paper cites Automatic expansion of a food image dataset leveraging existing categories with domain adaptation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Automatic expansion of a food image dataset leveraging existing categories with domain adaptation

Reference 25

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raw_fallback, observed 2026-08-06T19:53:39.494175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 98e40872-d594-4ece-b25b-dd867aa6d70a · outbound

This paper cites A hierarchical grocery store image dataset with visual and se- mantic labels.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights A hierarchical grocery store image dataset with visual and se- mantic labels

Reference 26

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raw_fallback, observed 2026-08-06T19:53:39.334140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation db3cc303-bb6a-401d-92a4-10614c58fe16 · outbound

This paper cites CounTR: Transformer-based Generalised Visual Counting.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights CounTR: Transformer-based Generalised Visual Counting

Reference 27

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no resolver link, observed 2026-08-06T19:53:26.321670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 655a8b2f-c5bb-4212-bc1b-757acbce9bb9 · outbound

This paper cites Ingredient prediction via context learn- ing network with class-adaptive asymmetric loss.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Ingredient prediction via context learn- ing network with class-adaptive asymmetric loss

Reference 28

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raw_fallback, observed 2026-08-06T19:53:39.046123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation fca39ed9-5124-4894-bbcf-16ec3630a1d5 · outbound

This paper cites Recipe1m+: A dataset for learning cross-modal embeddings for cooking recipes and food images.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Recipe1m+: A dataset for learning cross-modal embeddings for cooking recipes and food images

Reference 29

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raw_fallback, observed 2026-08-06T19:53:38.841539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:26.542477Z digest=sha256:004444ec3a1398e35e79f74fa5a587ef3ac2527a7d01ce4736dffe5f1216d7aa

Observation 1932c532-7b48-48d7-9228-b1e13c1dbfb8 · outbound

This paper cites Fruitnet: Indian fruits im- age dataset with quality for machine learning applications.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Fruitnet: Indian fruits im- age dataset with quality for machine learning applications

Reference 30

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raw_fallback, observed 2026-08-06T19:53:38.681540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:26.667179Z digest=sha256:f9688717da57572825b585a8d39f20a7e4170942d10154a5ddbf6c9a46fc54e0

Observation 7897c78d-d13b-409e-8bb8-ea5e06cd0ef6 · outbound

This paper cites Isia food- 500: A dataset for large-scale food recognition via stacked global-local attention network.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Isia food- 500: A dataset for large-scale food recognition via stacked global-local attention network

Reference 31

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raw_fallback, observed 2026-08-06T19:53:38.464346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:26.801394Z digest=sha256:cc9702d92363f3ecb6957c5b23f69acaa37bb4ee6df6b04a591a564713236f42

Observation 4791b073-32e1-458b-a08a-3c84b0f8d35b · outbound

This paper cites Large scale visual food recognition.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Large scale visual food recognition

Reference 32

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raw_fallback, observed 2026-08-06T19:53:38.225241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:26.931814Z digest=sha256:a4569d3f3a4ebbedc56d5c8b6a2a5c1edf32bd6babbcd441ed3a39a952a0ccd7

Observation ae1c6187-3828-44d9-8a00-8456315337df · outbound

This paper cites Fruits-262 dataset: A dataset containing a vast majority of the popular and known fruits, 2021.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Fruits-262 dataset: A dataset containing a vast majority of the popular and known fruits, 2021

Reference 33

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raw_fallback, observed 2026-08-06T19:53:37.906137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:27.059971Z digest=sha256:26e5cd95d0a8c3bda5e381c1d042225b63a3826fdebe5d2bcc77df854fc00267

Observation b5be9c7a-0031-4bf5-a487-572ff06d40d1 · outbound

This paper cites Using deep learning for image-based plant disease detection.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Using deep learning for image-based plant disease detection

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T19:53:27.179810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:53:27.179810Z digest=sha256:95886976af87f2a92052ef57aacfc04063580814bd2a2fdeca8f7d1f012c709a

Observation ddfa4779-5c8c-4ba7-8508-a9fc71506a73 · outbound

This paper cites Llava-chef: A multi- modal generative model for food recipes.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Llava-chef: A multi- modal generative model for food recipes

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-06T19:53:37.628796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:27.273778Z digest=sha256:e5b865b41c899c71689a8a7a289ff2066f044c3cce65404a45558ae1a2f82e4e

Observation bc9170a9-f3c8-44fd-b9d0-ff195124fb95 · outbound

This paper cites Omnicount: Multi-label object count- ing with semantic-geometric priors.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Omnicount: Multi-label object count- ing with semantic-geometric priors

Reference 36

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verified exact
raw_fallback, observed 2026-08-06T19:53:31.131578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:27.379914Z digest=sha256:5732f0b3c046c0e2645bec7fded847ac0246e7d168a596b1440b5692b6301120

Observation 049ee9d8-8b9c-40a1-b0f5-bc644c97a5c8 · outbound

This paper cites Terrace-based food counting and segmentation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Terrace-based food counting and segmentation

Reference 37

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verified fuzzy
raw_fallback, observed 2026-08-06T19:53:37.312089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:27.517857Z digest=sha256:53f0a3d111597fb373196ab7bca9edb414824a4cf6b4a75f7ffdd577f8839377

Observation 80585f7e-3df5-4ad7-8ff0-6c3b10f081c3 · outbound

This paper cites Sibnet: Food instance counting and segmentation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Sibnet: Food instance counting and segmentation

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:37.069751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:27.603573Z digest=sha256:f2a057e9357e341f485670591182453953d6ca5ec95930fbba572deaef603bf7

Observation eb867692-bfd8-4f6e-8caf-25413ae47dee · outbound

This paper cites Honey dataset stan- dard using hyperspectral imaging for machine learning prob- lems.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Honey dataset stan- dard using hyperspectral imaging for machine learning prob- lems

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:36.791037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:27.764729Z digest=sha256:5a49f87d4ef0546c4419c5836c264cd1025378bde9f3b37a941d27c42f803eef

Observation 3ff5d256-b4b2-4a24-9452-d21d213f5753 · outbound

This paper cites Uec-foodpix complete: A large-scale food image segmentation dataset.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Uec-foodpix complete: A large-scale food image segmentation dataset

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:36.510028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:27.904403Z digest=sha256:01eaeb000a4a3399eb98b376734d65573a5d47ae3d00836f54f8402102ad5e83

Observation 7fcfcdcf-f1b4-4cf9-b033-2d757bfaa3dc · outbound

This paper cites Foodd: food detection dataset for calorie mea- surement using food images.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Foodd: food detection dataset for calorie mea- surement using food images

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:36.345918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:27.990438Z digest=sha256:76e9806bb43689b327dc5ca1aa02da345a6ed6291cfadf8ca3aeb1b834b31235

Observation 505413ff-667f-4a50-beac-fec92ff09a79 · outbound

This paper cites Learning to count everything.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Learning to count everything

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:36.194324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:28.115644Z digest=sha256:6c3847193380e674bf57edf5c177a7283525027c5789d9681218bf71914e806f

Observation 1b3757c7-869b-4e61-9e7d-5f390cb534d0 · outbound

This paper cites Leveraging automatic personalised nutrition: food image recognition benchmark and dataset based on nutrition taxonomy.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Leveraging automatic personalised nutrition: food image recognition benchmark and dataset based on nutrition taxonomy

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:35.961390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:28.240031Z digest=sha256:704f8f526e731dbb557206b77f877a7c5b9b655fc550ae54476f50004d6c62f6

Observation a021bfa3-e0a4-4b9c-a568-14886c1a261b · outbound

This paper cites Multi-task learn- ing for calorie prediction on a novel large-scale recipe dataset enriched with nutritional information.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Multi-task learn- ing for calorie prediction on a novel large-scale recipe dataset enriched with nutritional information

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:35.728195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:28.340251Z digest=sha256:b59cde2d4edab192c4e26b2545bc4d66068eaa807b59de7e61ed1630b848f179

Observation 3b33dd40-68da-4aea-a358-0918a7107ddf · outbound

This paper cites Represent, compare, and learn: A similarity-aware framework for class-agnostic counting.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Represent, compare, and learn: A similarity-aware framework for class-agnostic counting

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:35.574649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:28.459533Z digest=sha256:0b079ceccdf19202866f94f449e0a5c95a72be827606f9468acbca426b7657e4

Observation 89722a38-d672-4e04-8177-9d8b45ce0b77 · outbound

This paper cites Plantdoc: A dataset for visual plant disease detection.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Plantdoc: A dataset for visual plant disease detection

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:35.323633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:28.573773Z digest=sha256:e836d6acbdc2675d82cff8883fea000fae74666bfbf927a9031265e9064b7872

Observation 17a11e7b-2365-4dc8-9d78-6f6e871ccc3e · outbound

This paper cites The cropandweed dataset: A multi-modal learning approach for efficient crop and weed manipulation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights The cropandweed dataset: A multi-modal learning approach for efficient crop and weed manipulation

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T19:53:28.672510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:53:28.672510Z digest=sha256:047f38702478c4ab4cd634b06a694ca23c13b784e918d11e86c132b43f452232

Observation 9eaa3be0-5a8d-4cc0-af57-c31be66bebc7 · outbound

This paper cites NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation

Reference 48

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:53:30.885920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:28.785531Z digest=sha256:5c103f542a76a82205149127a9088023c17d6beab9afcdc9b0bea4ec537ef54c

Observation b9550a4e-862b-4d03-a479-56fe48089bd0 · outbound

This paper cites Classification of biscuit defect states and foreign objects using cnn-based features.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Classification of biscuit defect states and foreign objects using cnn-based features

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:35.087350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:28.895200Z digest=sha256:a3e7828365da791c7e656de83fa895216d2dfa604d6377795d168e3f7b2b93bf

Observation 355f910f-24d0-466b-af96-bdcb2cf9445e · outbound

This paper cites Nutrition5k: To- wards automatic nutritional understanding of generic food.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Nutrition5k: To- wards automatic nutritional understanding of generic food

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:34.852539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:28.987157Z digest=sha256:662bdca746f73e07c252c966f0bd6c49e4ac2f6a0582500bac9ab70298f74adb

Observation c0309ff0-86cf-4bde-9ae6-20c746f2497c · outbound

This paper cites Rice seedling detection in uav images using transfer learning and machine learning.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Rice seedling detection in uav images using transfer learning and machine learning

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:34.661513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:29.088218Z digest=sha256:5a172328082e44b71d19982d4cc5744e536a03659cc9d491ad88a9574d052297

Observation de0a6269-a8f4-4808-a1f1-48d221ed31ec · outbound

This paper cites Solov2: Dynamic and fast instance segmenta- tion.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Solov2: Dynamic and fast instance segmenta- tion

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:34.394415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:29.199151Z digest=sha256:1a3a5d2dbeb76c23d8b826c9c20a51361e12be54a9ec7b8dd26a28b00988bd3d

Observation c7ded2d5-9f00-4262-b816-cf90a767323c · outbound

This paper cites Multi-state ingredient recognition via adaptive multi-centric network.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Multi-state ingredient recognition via adaptive multi-centric network

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:34.229069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:29.290818Z digest=sha256:7951897bb235c16152b28d1c87e1c1450ccd187a0444d74a28ecdf3d23dc1858

Observation bdfc3e80-2140-4118-b0b3-b034fa791bcf · outbound

This paper cites Automatic counting of in situ rice seedlings from uav images based on a deep fully convolutional neural network.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Automatic counting of in situ rice seedlings from uav images based on a deep fully convolutional neural network

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:34.027399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:29.423316Z digest=sha256:5a881bd06cf60f4e23137e419bc7bcf673f16b4339c5b110a6badae031a7fc06

Observation 1d793bfb-7af9-4384-9797-6ceae18f0e74 · outbound

This paper cites A large-scale benchmark for food im- age segmentation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights A large-scale benchmark for food im- age segmentation

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:33.761871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:29.547638Z digest=sha256:d858eeb78dbaf64193cc4f8fdbaa2855a4bb9b83798291ac57c7650baf5c210c

Observation a2377af5-c08e-46bb-b898-1abb3dbe559e · outbound

This paper cites Hsifoodingr-64: A dataset for hyperspectral food-related studies and a benchmark method on food ingredient retrieval.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Hsifoodingr-64: A dataset for hyperspectral food-related studies and a benchmark method on food ingredient retrieval

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:33.503989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:29.681511Z digest=sha256:9c1a1254b81ba2213a7066050efaf95543d0e921cc32b952c005fb9a0fe82cc6

Observation 2fc314d1-32f5-4dbc-bc48-0b40635357c3 · outbound

This paper cites Multiple attentional pyra- mid networks for chinese herbal recognition.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Multiple attentional pyra- mid networks for chinese herbal recognition

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:33.312453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:29.819436Z digest=sha256:e99aaffd4c622f396b164514d3ce5adc99050749f15734f5148990d288b659d3

Observation 54e9e856-c769-4a4c-a3fa-02ba0df1f4d4 · outbound

This paper cites FoodLMM: A Versatile Food Assistant using Large Multi-modal Model.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights FoodLMM: A Versatile Food Assistant using Large Multi-modal Model

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T19:53:29.966947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:53:29.966947Z digest=sha256:f5bb8ddc62bcb5a51fbac0108ac925ea80a5abb5c883234a8b8d4ccfe7921eb1

Observation bdc040f6-15b7-47d0-8e33-c3ad6c81a8f3 · outbound

This paper cites Fine-grained image classifi- cation by exploring bipartite-graph labels.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Fine-grained image classifi- cation by exploring bipartite-graph labels

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:33.027010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:30.054060Z digest=sha256:6b9ec279c97a4f111143b27dda05fea9bf4a5219fd6fedf0c55d87622b1e4fd9

Observation 63074190-482e-4b65-af61-83c56394b3f4 · outbound

This paper cites FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination

Reference 60

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:53:30.698067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:30.110308Z digest=sha256:67dde90c07fc8cbc0a2d4e971ed79c6d54cfe5bcd2668d2b70e4885640e1206a

Observation eebb64af-2f74-4a5d-8395-05255c607a05 · outbound

This paper cites Learn more for food recognition via progressive self-distillation.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Learn more for food recognition via progressive self-distillation

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:32.796328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:30.181170Z digest=sha256:b630f437a04eeb1e564391ce4de620670743e592ee48210e978df98323024a17

Observation bba88002-894d-4fa0-b8cc-4480198e88ca · outbound

This paper cites It is worth noting that these categories do not represent all foods.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights It is worth noting that these categories do not represent all foods

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:32.611162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:30.299412Z digest=sha256:460e5673de1cd1dd389086730a11ea1fd556385d9aa109e6361c6e55e9a96592

Observation 8cc279fd-271d-41cc-9b8e-42cbb916967b · outbound

This paper cites an unresolved cited work.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Unresolved cited work

Reference 63

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:53:32.362474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:30.385245Z digest=sha256:5951a15133bb64306093e8137d5c452b4c883faeef2947b55f5758317cbe67c7

Observation 10e8cd40-c7fe-4a6a-bb04-22de95b0e843 · outbound

This paper cites 2), we have obtained spectral data with wavelength between 400 nm to 1020 nm shown in Table 8.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights 2), we have obtained spectral data with wavelength between 400 nm to 1020 nm shown in Table 8

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:53:32.141100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:30.462315Z digest=sha256:ec1290305f885da137560d23a8409bda7b1d085c6e16d4929b316550e615624a

Observation 566bf4cc-965e-4d73-8b88-6f96a0bcabf7 · outbound

This paper cites an unresolved cited work.

SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights Unresolved cited work

Reference 65

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:53:31.851022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T19:53:30.508953Z digest=sha256:80c750098f1e47fb75a0d802b50fb5868a4cd0e36137b23a5d34c1c85ab95562

Pith citing papers

No inbound Pith citation observations are available.