On the public citation graph

Food and Ingredient Joint Learning for Fine-Grained Recognition

https://doi.org/10.1109/tcsvt.2020.3020079

0 signed · 126 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01264-9_26openalex-snapshot

Learning to Navigate for Fine-Grained Classification

inferredcites →https://doi.org/10.1007/978-3-319-10590-1_54openalex-snapshot

Part-Based R-CNNs for Fine-Grained Category Detection

inferredcites →https://doi.org/10.1007/978-3-319-67389-9_44openalex-snapshot

Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks

inferredcites →https://doi.org/10.1016/j.patcog.2017.10.002openalex-snapshot

Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization

inferredcites →https://doi.org/10.1109/3dv.2016.79openalex-snapshot

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2015.7299023openalex-snapshot

A large-scale car dataset for fine-grained categorization and verification

inferredcites →https://doi.org/10.1109/cvpr.2015.7299194openalex-snapshot

Fine-grained recognition without part annotations

inferredcites →https://doi.org/10.1109/cvpr.2016.127openalex-snapshot

Fine-Grained Image Classification by Exploring Bipartite-Graph Labels

inferredcites →https://doi.org/10.1109/cvpr.2016.129openalex-snapshot

SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition

inferredcites →https://doi.org/10.1109/cvpr.2016.131openalex-snapshot

Mining Discriminative Triplets of Patches for Fine-Grained Classification

inferredcites →https://doi.org/10.1109/cvpr.2016.319openalex-snapshot

Learning Deep Features for Discriminative Localization

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.325openalex-snapshot

Kernel Pooling for Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2017.476openalex-snapshot

Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.743openalex-snapshot

Low-Rank Bilinear Pooling for Fine-Grained Classification

inferredcites →https://doi.org/10.1109/cvpr.2017.775openalex-snapshot

Fine-Grained Image Classification via Combining Vision and Language

inferredcites →https://doi.org/10.1109/cvpr.2019.00515openalex-snapshot

Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2019.00530openalex-snapshot

Destruction and Construction Learning for Fine-Grained Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2019.01070openalex-snapshot

Inverse Cooking: Recipe Generation From Food Images

inferredcites →https://doi.org/10.1109/cvpr.2019.01096openalex-snapshot

Attention Branch Network: Learning of Attention Mechanism for Visual Explanation

inferredcites →https://doi.org/10.1109/cvprw56347.2022.00309openalex-snapshot

ResNeSt: Split-Attention Networks

inferredcites →https://doi.org/10.1109/iccv.2015.276openalex-snapshot

Multiple Granularity Descriptors for Fine-Grained Categorization

inferredcites →https://doi.org/10.1109/iccv.2017.324openalex-snapshot

Focal Loss for Dense Object Detection

inferredcites →https://doi.org/10.1109/iccv.2017.557openalex-snapshot

Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition

inferredcites →https://doi.org/10.1109/iccv.2017.63openalex-snapshot

Higher-Order Integration of Hierarchical Convolutional Activations for Fine-Grained Visual Categorization

inferredcites →https://doi.org/10.1109/iccvw.2013.77openalex-snapshot

3D Object Representations for Fine-Grained Categorization

inferredcites →https://doi.org/10.1109/icmew.2015.7169757openalex-snapshot

Recipe recognition with large multimodal food dataset

inferredcites →https://doi.org/10.1109/isbi.2019.8759329openalex-snapshot

A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation

inferredcites →https://doi.org/10.1109/tcsvt.2019.2902268openalex-snapshot

Task-Aware Attention Model for Clothing Attribute Prediction

inferredcites →https://doi.org/10.1109/tip.2017.2774041openalex-snapshot

Object-Part Attention Model for Fine-Grained Image Classification

inferredcites →https://doi.org/10.1109/tip.2019.2929447openalex-snapshot

Multi-Scale Multi-View Deep Feature Aggregation for Food Recognition

inferredcites →https://doi.org/10.1109/tmm.2017.2648498openalex-snapshot

Diversified Visual Attention Networks for Fine-Grained Object Classification

inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

inferredcites →https://doi.org/10.1109/tpami.2017.2723400openalex-snapshot

Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition

inferredcites →https://doi.org/10.1145/2638728.2641339openalex-snapshot

Food image recognition with deep convolutional features

inferredcites →https://doi.org/10.1145/2964284.2964315openalex-snapshot

Deep-based Ingredient Recognition for Cooking Recipe Retrieval

inferredcites →https://doi.org/10.1145/3123266.3123428openalex-snapshot

Cross-modal Recipe Retrieval with Rich Food Attributes

inferredcites →https://doi.org/10.1145/3123266.3126490openalex-snapshot

PIC2DISH

inferredcites →https://doi.org/10.1145/3240508.3240550openalex-snapshot

Attribute-Aware Attention Model for Fine-grained Representation Learning

inferredcites →https://doi.org/10.1145/3329168openalex-snapshot

A Survey on Food Computing

inferredcites →https://doi.org/10.1145/3343031.3350948openalex-snapshot

Ingredient-Guided Cascaded Multi-Attention Network for Food Recognition

inferredcites →https://doi.org/10.1609/aaai.v32i1.12295openalex-snapshot

Audio Visual Attribute Discovery for Fine-Grained Object Recognition

inferredcites →https://doi.org/10.23919/mva.2017.7986835openalex-snapshot

Simultaneous estimation of food categories and calories with multi-task CNN

inferredcites →https://doi.org/10.4236/jcc.2020.84003openalex-snapshot

An SLGC Model for Asian Food Image Classification

inferredcites →https://doi.org/10.48550/arxiv.1306.5151openalex-snapshot

Fine-Grained Visual Classification of Aircraft

inferredcites →https://doi.org/10.48550/arxiv.1406.2952openalex-snapshot

Bird Species Categorization Using Pose Normalized Deep Convolutional Nets

inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot

Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1603.06765openalex-snapshot

Fully Convolutional Attention Networks for Fine-Grained Recognition

inferredcites →https://doi.org/10.48550/arxiv.2004.01963openalex-snapshot

Fine grained classification for multi-source land cover mapping

inferredcites →https://doi.org/10.48550/arxiv.2207.03692openalex-snapshot

Mining Discriminative Food Regions for Accurate Food Recognition

inferredcites →https://doi.org/10.57702/i9oeewf6openalex-snapshot

Caltech-UCSD Birds-200-2011 Dataset

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4757424openalex-extracted

Rethinking Attention Mechanism: Channel Re-Attention and Spatial Multi-Region Attention for Fine-Grained Visual Classification

inferred← citeshttps://doi.org/10.1002/advs.202403578openalex-snapshot

Integrating Vision‐Language Models for Accelerated High‐Throughput Nutrition Screening

inferred← citeshttps://doi.org/10.1007/978-3-030-92632-8_31openalex-snapshot

A Detection Network United Local Feature Points and Components for Fine-Grained Image Classification

inferred← citeshttps://doi.org/10.1007/978-3-031-04881-4_52openalex-snapshot

Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition

inferred← citeshttps://doi.org/10.1007/978-3-031-22959-6_3openalex-snapshot

Digital Twins for Food Nutrition and Health Based on Cloud Communication

inferred← citeshttps://doi.org/10.1007/s00371-024-03546-5openalex-snapshot

Deep attentive multimodal learning for food information enhancement via early-stage heterogeneous fusion

inferred← citeshttps://doi.org/10.1007/s00521-023-09349-4openalex-snapshot

Foodnet: multi-scale and label dependency learning-based multi-task network for food and ingredient recognition

inferred← citeshttps://doi.org/10.1007/s00530-024-01297-wopenalex-snapshot

Recognize after early fusion: the Chinese food recognition based on the alignment of image and ingredients

inferred← citeshttps://doi.org/10.1007/s00530-024-01354-4openalex-snapshot

MTKGR: multi-task knowledge graph reasoning for food and ingredient recognition

inferred← citeshttps://doi.org/10.1007/s00530-026-02525-1openalex-snapshot

CTNet: learning local details and global context for food recognition

inferred← citeshttps://doi.org/10.1007/s10489-022-03248-2openalex-snapshot

Dual-attention guided multi-scale network for single image super-resolution

inferred← citeshttps://doi.org/10.1007/s11042-023-14373-6openalex-snapshot

The multi-learning for food analyses in computer vision: a survey

inferred← citeshttps://doi.org/10.1007/s11042-023-17757-wopenalex-snapshot

Multi-food detection using a modified swin-transfomer with recursive feature pyramid network

inferred← citeshttps://doi.org/10.1007/s11694-025-03439-8openalex-snapshot

FGFoodNet: Ingredient-perceived fine-grained food recognition for dietary monitoring

inferred← citeshttps://doi.org/10.1007/s11694-026-04637-8openalex-snapshot

CRD-DETR: a Chinese dish detection network based on triple enhancement mechanisms

inferred← citeshttps://doi.org/10.1007/s11694-026-04881-yopenalex-snapshot

Frequency-aware adaptive modeling for image-based fine-grained food recognition and characterization

inferred← citeshttps://doi.org/10.1007/s11760-023-02600-4openalex-snapshot

DEEPFIC: food item classification with calorie calculation using dragonfly deep learning network

inferred← citeshttps://doi.org/10.1007/s13042-025-02892-7openalex-snapshot

SGC-Net: an effective fine-grained visual classification framework via self-guided contrast regularization and feature boosting

inferred← citeshttps://doi.org/10.1016/j.asoc.2022.109281openalex-snapshot

Multimodal feature fusion and exploitation with dual learning and reinforcement learning for recipe generation

inferred← citeshttps://doi.org/10.1016/j.eswa.2026.133189openalex-snapshot

Density-aware adaptive label assignment for end-to-end dense detection in drone images

inferred← citeshttps://doi.org/10.1016/j.imavis.2023.104753openalex-snapshot

Learning more discriminative clues with gradual attention for fine-grained visual categorization

inferred← citeshttps://doi.org/10.1016/j.iswa.2026.200632openalex-snapshot

A study on the generalization of DINOv2 features for food recognition tasks: A unified evaluation framework

inferred← citeshttps://doi.org/10.1016/j.knosys.2020.106663openalex-snapshot

Kernel-attended residual network for single image super-resolution

inferred← citeshttps://doi.org/10.1016/j.knosys.2024.111685openalex-snapshot

Overlapping object detection with adaptive Gaussian sample division and asymmetric weighted loss

inferred← citeshttps://doi.org/10.1016/j.neucom.2021.02.008openalex-snapshot

Image super-resolution based on residually dense distilled attention network

inferred← citeshttps://doi.org/10.1016/j.neucom.2021.04.102openalex-snapshot

Preparing lessons: Improve knowledge distillation with better supervision

inferred← citeshttps://doi.org/10.1038/s41598-025-32610-wopenalex-snapshot

Enhancing dietary management and nutritional analysis through deep learning: a multi-model approach for food classification and volume estimation

inferred← citeshttps://doi.org/10.1080/21681163.2022.2099301openalex-snapshot

Dietary assessment framework using improved K-means clustering and optimisation assisted CNN

inferred← citeshttps://doi.org/10.1109/access.2023.3243243openalex-snapshot

HSIFoodIngr-64: A Dataset for Hyperspectral Food-Related Studies and a Benchmark Method on Food Ingredient Retrieval

inferred← citeshttps://doi.org/10.1109/icccnt61001.2024.10725510openalex-snapshot

Online Food Order Management System With Recommendation Integrated ASP.Net and Microsoft SQL Server

inferred← citeshttps://doi.org/10.1109/iccv48922.2021.00699openalex-snapshot

AINet: Association Implantation for Superpixel Segmentation

inferred← citeshttps://doi.org/10.1109/icicacs57338.2023.10099841openalex-snapshot

Fine Tuned DBN Model for Food Ingredient Recognition: Introduction to Self-improved Tasmanian Devil Optimization Algorithm

inferred← citeshttps://doi.org/10.1109/icme59968.2025.11209410openalex-snapshot

Efficient Prompt Tuning for Hierarchical Ingredient Recognition

inferred← citeshttps://doi.org/10.1109/isctech63666.2024.10845333openalex-snapshot

Lightweight Food Image Recognition Algorithm Based on YOLOv8

inferred← citeshttps://doi.org/10.1109/ises54909.2022.00152openalex-snapshot

iLog 2.0: A Novel Method for Food Nutritional Value Automatic Quantification in Smart Healthcare

inferred← citeshttps://doi.org/10.1109/jcsse68839.2026.11596987openalex-snapshot

MacroVision: A Two-Stage Localization and Fine-Grained Classification Pipeline for High-Cardinality Food Ingredient Recognition

inferred← citeshttps://doi.org/10.1109/tcsvt.2021.3069835openalex-snapshot

Dynamic Perception Framework for Fine-Grained Recognition

inferred← citeshttps://doi.org/10.1109/tcsvt.2021.3088446openalex-snapshot

Occlusion-Sensitive Person Re-Identification via Attribute-Based Shift Attention

inferred← citeshttps://doi.org/10.1109/tcsvt.2021.3134129openalex-snapshot

2021 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 31

inferred← citeshttps://doi.org/10.1109/tcsvt.2022.3218790openalex-snapshot

Sequential Learning for Ingredient Recognition From Images

inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3263870openalex-snapshot

Semantic-Guided Information Alignment Network for Fine-Grained Image Recognition

inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3284405openalex-snapshot

The Image Data and Backbone in Weakly Supervised Fine-Grained Visual Categorization: A Revisit and Further Thinking

inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3291054openalex-snapshot

Improved Continually Evolved Classifiers for Few-Shot Class-Incremental Learning

inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3295432openalex-snapshot

AJENet: Adaptive Joints Enhancement Network for Abnormal Behavior Detection in Office Scenario

inferred← citeshttps://doi.org/10.1109/tii.2023.3333935openalex-snapshot

Multi-state Ingredient Recognition via Adaptive Multi-centric Network

inferred← citeshttps://doi.org/10.1109/tip.2021.3049963openalex-snapshot

Crowd Scene Analysis Encounters High Density and Scale Variation

inferred← citeshttps://doi.org/10.1109/tip.2022.3193763openalex-snapshot

Ingredient-Guided Region Discovery and Relationship Modeling for Food Category-Ingredient Prediction

inferred← citeshttps://doi.org/10.1109/tip.2023.3318958openalex-snapshot

Ingredient Prediction via Context Learning Network With Class-Adaptive Asymmetric Loss

inferred← citeshttps://doi.org/10.1109/tip.2024.3374211openalex-snapshot

Convolution-Enhanced Bi-Branch Adaptive Transformer With Cross-Task Interaction for Food Category and Ingredient Recognition

inferred← citeshttps://doi.org/10.1109/tmm.2022.3207895openalex-snapshot

MPPM: A Mobile-Efficient Part Model for Object re-ID

inferred← citeshttps://doi.org/10.1109/tmm.2024.3521808openalex-snapshot

Towards Better Distortion Feature Learning for Object Detection in Top-View Fisheye Cameras

inferred← citeshttps://doi.org/10.1109/tmm.2025.3543067openalex-snapshot

Threefold Encoder Interaction: Hierarchical Multi-Grained Semantic Alignment for Cross-Modal Food Retrieval

inferred← citeshttps://doi.org/10.1109/tmm.2025.3590924openalex-snapshot

FoodLMM: A Versatile Food Assistant Using Large Multi-Modal Model

inferred← citeshttps://doi.org/10.1109/tmm.2026.3682155openalex-snapshot

Prompt-Based Task-Conditioning Transformer for Low-Quality Detection

inferred← citeshttps://doi.org/10.1109/wacv61041.2025.00244openalex-snapshot

Retrieval Augmented Recipe Generation

inferred← citeshttps://doi.org/10.1145/3508546.3508577openalex-snapshot

Region-Level Attention Network for Food and Ingredient Joint Recognition

inferred← citeshttps://doi.org/10.1145/3552484.3555754openalex-snapshot

Learning Multi-Subset of Classes for Fine-Grained Food Recognition

inferred← citeshttps://doi.org/10.1145/3554738openalex-snapshot

ML-CookGAN: Multi-Label Generative Adversarial Network for Food Image Generation

inferred← citeshttps://doi.org/10.1145/3581783.3612234openalex-snapshot

Consistency-aware Feature Learning for Hierarchical Fine-grained Visual Classification

inferred← citeshttps://doi.org/10.1145/3607828.3617795openalex-snapshot

An Improved Encoder-Decoder Framework for Food Energy Estimation

inferred← citeshttps://doi.org/10.1145/3607828.3617798openalex-snapshot

Muti-Stage Hierarchical Food Classification

inferred← citeshttps://doi.org/10.1145/3664647.3680977openalex-snapshot

Navigating Weight Prediction with Diet Diary

inferred← citeshttps://doi.org/10.1145/3771995openalex-snapshot

CookingDiffusion: Cooking Procedural Image Generation with Stable Diffusion

inferred← citeshttps://doi.org/10.1371/journal.pone.0358248openalex-snapshot

Fine-grained Chinese dish recognition on CNFOOD-241: A discriminative learning approach for improving category-level discrimination

inferred← citeshttps://doi.org/10.1609/aaai.v37i3.25501openalex-snapshot

Learn More for Food Recognition via Progressive Self-Distillation

inferred← citeshttps://doi.org/10.19101/ijacr.2021.1152006openalex-snapshot

Food recognition improvement by using hyper-spectral imagery

inferred← citeshttps://doi.org/10.2493/jjspe.89.956openalex-snapshot

Category and Ingredients Estimation From Food Image Robust to Typicality of Used Ingredients

inferred← citeshttps://doi.org/10.32657/10356/161182openalex-snapshot

Visual food recognition using artificial intelligence

inferred← citeshttps://doi.org/10.3390/app10238667openalex-snapshot

A Sample Weight and AdaBoost CNN-Based Coarse to Fine Classification of Fruit and Vegetables at a Supermarket Self-Checkout

inferred← citeshttps://doi.org/10.3390/app11209562openalex-snapshot

Progressive Kernel Extreme Learning Machine for Food Image Analysis via Optimal Features from Quality Resilient CNN

inferred← citeshttps://doi.org/10.3390/electronics10111234openalex-snapshot

A Lightweight Dense Connected Approach with Attention on Single Image Super-Resolution

inferred← citeshttps://doi.org/10.3390/foods14244269openalex-snapshot

Visual Food Ingredient Prediction Using Deep Learning with Direct F-Score Optimization

inferred← citeshttps://doi.org/10.3390/healthcare9121676openalex-snapshot

A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

inferred← citeshttps://doi.org/10.3390/nu17020362openalex-snapshot

An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition

inferred← citeshttps://doi.org/10.5772/intechopen.114024openalex-snapshot

Visual Recognition of Food Ingredients: A Systematic Review

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.