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Found 19 papers, 10 papers with code
Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection
The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories.
Vision-based Food Nutrition Estimation via RGB-D Fusion Network
In this paper, we designed one RGB-D fusion network, which integrated multimodal feature fusion (MMFF) and multi-scale fusion for visioin-based nutrition assessment.
SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection
To tackle this, we propose the Semantic Separable Diffusion Synthesizer (SeeDS) framework for Zero-Shot Food Detection (ZSFD).
Ranked #1 on
Generalized Zero-Shot Object Detection
on MS-COCO
Towards Food Image Retrieval via Generalization-oriented Sampling and Loss Function Design
Therefore, it is challenging to study the generalization of the model in food image retrieval.
Vision-Based Fruit Recognition via Multi-Scale Attention CNN
Fruit quality assessment, grading and sorting are of vital importance to fruit processing, and all these involve fruit recognition.
Deep Learning for Logo Detection: A Survey
When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks.
Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection
Unlike general object detection, logo detection is a challenging task, especially for small logo objects and large aspect ratio logo objects in the real-world scenario.
FoodLogoDet-1500: A Dataset for Large-Scale Food Logo Detection via Multi-Scale Feature Decoupling Network
For that, we propose a novel food logo detection method Multi-scale Feature Decoupling Network (MFDNet), which decouples classification and regression into two branches and focuses on the classification branch to solve the problem of distinguishing multiple food logo categories.
A review on vision-based analysis for automatic dietary assessment
We also provide the latest ideas for future development of VBDA, e. g., fine-grained food analysis and accurate volume estimation.
Applications of knowledge graphs for food science and industry
The deployment of various networks (e. g., Internet of Things [IoT] and mobile networks), databases (e. g., nutrition tables and food compositional databases), and social media (e. g., Instagram and Twitter) generates huge amounts of food data, which present researchers with an unprecedented opportunity to study various problems and applications in food science and industry via data-driven computational methods.
Large Scale Visual Food Recognition
Food2K can be further explored to benefit more food-relevant tasks including emerging and more complex ones (e. g., nutritional understanding of food), and the trained models on Food2K can be expected as backbones to improve the performance of more food-relevant tasks.
Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough
Limited by the definition of AP, such methods consider both negative and positive instances ranking before each positive instance.
Ranked #3 on
Vehicle Re-Identification
on VehicleID Large
Dataset Bias in Few-shot Image Recognition
Second, we investigate performance differences on different datasets from dataset structures and different few-shot learning methods.
ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network
To encourage further progress in food recognition, we introduce the dataset ISIA Food- 500 with 500 categories from the list in the Wikipedia and 399, 726 images, a more comprehensive food dataset that surpasses existing popular benchmark datasets by category coverage and data volume.
LogoDet-3K: A Large-Scale Image Dataset for Logo Detection
LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets.
Ranked #1 on
Object Detection
on FlickrLogos-32
Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification
Moreover, we propose a Discriminative Region Navigation and Augmentation Network (DRNA-Net), which is capable of discovering more informative logo regions and augmenting these image regions for logo classification.
Food Recommendation: Framework, Existing Solutions and Challenges
A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e. g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat.
A Survey on Food Computing
This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.
Computers and Society Multimedia
Food recognition and recipe analysis: integrating visual content, context and external knowledge
The central role of food in our individual and social life, combined with recent technological advances, has motivated a growing interest in applications that help to better monitor dietary habits as well as the exploration and retrieval of food-related information.
