Food Recognition
11 papers with code • 0 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Food Recognition
Datasets
Recipe1M+
FoodX-251
ISIA Food-500
ChineseFoodNet
KenyanFood13
CNFOOD-241-Chen
Indian Food Image Dataset
Latest papers
Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning
Our findings elucidate that our proposed methodology establishes a new benchmark for SOTA performance in food recognition on the CNFOOD-241 dataset.
Feature-Suppressed Contrast for Self-Supervised Food Pre-training
liuxindazz/FeaSC
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As the similar contents of the two views are salient or highly responsive in the feature map, the proposed FeaSC uses a response-aware scheme to localize salient features in an unsupervised manner.
A Central Asian Food Dataset for Personalized Dietary Interventions, Extended Abstract
is2ai/central-asian-food-dataset
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Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms.
A Central Asian Food Dataset for Personalized Dietary Interventions
is2ai/central-asian-food-dataset
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MDPI: Nutrients 2023
Nowadays, it is common for people to take photographs of every beverage, snack, or meal they eat and then post these photographs on social media platforms.
Vision and Structured-Language Pretraining for Cross-Modal Food Retrieval
mshukor/vlpcook
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Finally, we validate the generalization of the approach to other tasks (i. e, Food Recognition) and domains with structured text such as the Medical domain on the ROCO dataset.
Food Ingredients Recognition through Multi-label Learning
RameezI/dishscan
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Within this framework, we focus on one of the core functionalities to visually recognize various ingredients.
Learning Multi-Subset of Classes for Fine-Grained Food Recognition
javierrodenas/Learning-Multi-Subset-of-Classes-for-Fine-Grained-Recognition
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30th ACM International Conference on Multimedia 2022
We validated our proposed method using two recent state-of-the-art vision transformers on three public food recognition datasets.
Mining Discriminative Food Regions for Accurate Food Recognition
Taking inspiration from Adversarial Erasing, a strategy that progressively discovers discriminative object regions for weakly supervised semantic segmentation, we propose a novel network architecture in which a primary network maintains the base accuracy of classifying an input image, an auxiliary network adversarially mines discriminative food regions, and a region network classifies the resulting mined regions.
Deep learning approaches in food recognition
chairiq/FoodCNNs
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Automatic image-based food recognition is a particularly challenging task.
NU-InNet: Thai Food Image Recognition Using Convolutional Neural Networks on Smartphone
In this paper, the image recognition for Thai food using a smartphone is studied.
