Papers Food Recognition
“Food Recognition” 태그가 달린 논문 49편 · 필터 해제
An Explorative Analysis of SVM Classifier and ResNet50 Architecture on African Food Classification
Food recognition systems has advanced significantly for Western cuisines, yet its application to African foods remains underexplored. This study addresses this gap by evaluating both deep learning and traditional machine…
Food RecognitionBenchmarking Post-Hoc Unknown-Category Detection in Food Recognition
Food recognition models often struggle to distinguish between seen and unseen samples, frequently misclassifying samples from unseen categories by assigning them an in-distribution (ID) label. This misclassification pres…
BenchmarkingFood RecognitionOut of Distribution (OOD) DetectionImproving Food Image Recognition with Noisy Vision Transformer
Food image recognition is a challenging task in computer vision due to the high variability and complexity of food images. In this study, we investigate the potential of Noisy Vision Transformers (NoisyViT) for improving…
Food RecognitionChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation
Significant work has been conducted in the domain of food computing, yet these studies typically focus on single tasks such as t2t (instruction generation from food titles and ingredients), i2t (recipe generation from fo…
DecoderFood RecognitionImage GenerationRecipe GenerationNutrifyAI: An AI-Powered System for Real-Time Food Detection, Nutritional Analysis, and Personalized Meal Recommendations
With diet and nutrition apps reaching 1.4 billion users in 2022 [1], it's not surprise that popular health apps, MyFitnessPal, Noom, and Calorie Counter, are surging in popularity. However, one major setback [2] of nearl…
Food RecognitionNutritionComputer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2
In contemporary society, the application of artificial intelligence for automatic food recognition offers substantial potential for nutrition tracking, reducing food waste, and enhancing productivity in food production a…
Data AugmentationFood RecognitionNutritionTransfer LearningLearning to Classify New Foods Incrementally Via Compressed Exemplars
Food image classification systems play a crucial role in health monitoring and diet tracking through image-based dietary assessment techniques. However, existing food recognition systems rely on static datasets character…
Continual LearningFood Recognitionimage-classificationImage Classification+1GCAM: Gaussian and causal-attention model of food fine-grained recognition
Currently, most food recognition relies on deep learning for category classification. However, these approaches struggle to effectively distinguish between visually similar food samples, highlighting the pressing need to…
counterfactualCounterfactual ReasoningFine-Grained Image RecognitionFood RecognitionFrom Canteen Food to Daily Meals: Generalizing Food Recognition to More Practical Scenarios
The precise recognition of food categories plays a pivotal role for intelligent health management, attracting significant research attention in recent years. Prominent benchmarks, such as Food-101 and VIREO Food-172, pro…
Food RecognitionRes-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning
Food classification is the foundation for developing food vision tasks and plays a key role in the burgeoning field of computational nutrition. Due to the complexity of food requiring fine-grained classification, recent …
ClassificationFine-Grained Image RecognitionFood RecognitionMamba+2FoodLMM: A Versatile Food Assistant using Large Multi-modal Model
Large Multi-modal Models (LMMs) have made impressive progress in many vision-language tasks. Nevertheless, the performance of general LMMs in specific domains is still far from satisfactory. This paper proposes FoodLMM, …
Food RecognitionMulti-Task LearningNutritionReasoning Segmentation+2Dining on Details: LLM-Guided Expert Networks for Fine-Grained Food Recognition
In the field of fine-grained food recognition, subset learning-based methods offer a strategic approach that groups classes into subsets to guide the training process. Our study introduces a novel approach, referred to a…
Fine-Grained Image ClassificationFine-Grained Image RecognitionFood RecognitionMulti-Task LearningFeature-Suppressed Contrast for Self-Supervised Food Pre-training
Most previous approaches for analyzing food images have relied on extensively annotated datasets, resulting in significant human labeling expenses due to the varied and intricate nature of such images. Inspired by the ef…
Food RecognitionSelf-Supervised LearningLong-Tailed Continual Learning For Visual Food Recognition
Deep learning based food recognition has achieved remarkable progress in predicting food types given an eating occasion image. However, there are two major obstacles that hinder deployment in real world scenario. First, …
Continual LearningData AugmentationFood RecognitionKnowledge DistillationFood Recognition and Nutritional Apps
Food recognition and nutritional apps are trending technologies that may revolutionise the way people with diabetes manage their diet. Such apps can monitor food intake as a digital diary and even employ artificial intel…
Food RecognitionNutritionA Central Asian Food Dataset for Personalized Dietary Interventions, Extended Abstract
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. Leveraging these social trends, real-time food recognition and r…
Food RecognitionA Central Asian Food Dataset for Personalized Dietary Interventions
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. Leveraging these social trends, real-time food recognition and r…
Food RecognitionLearn More for Food Recognition via Progressive Self-Distillation
Food recognition has a wide range of applications, such as health-aware recommendation and self-service restaurants. Most previous methods of food recognition firstly locate informative regions in some weakly-supervised …
Food RecognitionFrom Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore
Singapore has been striving to improve the provision of healthcare services to her people. In this course, the government has taken note of the deficiency in regulating and supervising people's nutrient intake, which is …
Contrastive LearningFood RecognitionManagementNutritionVision and Structured-Language Pretraining for Cross-Modal Food Retrieval
Vision-Language Pretraining (VLP) and Foundation models have been the go-to recipe for achieving SoTA performance on general benchmarks. However, leveraging these powerful techniques for more complex vision-language task…
Cross-Modal RetrievalFood RecognitionRetrieval