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Data-driven Meta-set Based Fine-Grained Visual Recognition

https://doi.org/10.1145/3394171.3414044
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What this work says

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

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International Journal of Computer Vision
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Part-Based R-CNNs for Fine-Grained Category Detection
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The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
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Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization
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Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection
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Hyper-class augmented and regularized deep learning for fine-grained image classification
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Visual recognition by learning from web data: A weakly supervised domain generalization approach
inferredcites →https://doi.org/10.1109/cvpr.2016.128openalex-snapshot
Picking Deep Filter Responses for Fine-Grained Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.130openalex-snapshot
Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning with Humans in the Loop
inferredcites →https://doi.org/10.1109/cvpr.2016.132openalex-snapshot
Part-Stacked CNN for Fine-Grained Visual Categorization
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
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.2017.688openalex-snapshot
Fine-Grained Recognition as HSnet Search for Informative Image Parts
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.2018.00436openalex-snapshot
Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00749openalex-snapshot
Webly Supervised Learning Meets Zero-shot Learning: A Hybrid Approach for Fine-Grained Classification
inferredcites →https://doi.org/10.1109/cvpr.2019.00315openalex-snapshot
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up
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.2019.00961openalex-snapshot
Attentive Region Embedding Network for Zero-Shot Learning
inferredcites →https://doi.org/10.1109/cvpr20399.2014openalex-snapshot
2014 IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/iccv.2015.170openalex-snapshot
Bilinear CNN Models for Fine-Grained Visual Recognition
inferredcites →https://doi.org/10.1109/iccv.2015.290openalex-snapshot
Augmenting Strong Supervision Using Web Data for Fine-Grained Categorization
inferredcites →https://doi.org/10.1109/iccv.2017.211openalex-snapshot
Learning from Noisy Labels with Distillation
inferredcites →https://doi.org/10.1109/iccv.2019.00976openalex-snapshot
SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering
inferredcites →https://doi.org/10.1109/iccv19984.2013openalex-snapshot
2013 IEEE International Conference on Computer Vision
inferredcites →https://doi.org/10.1109/iccvw.2013.77openalex-snapshot
3D Object Representations for Fine-Grained Categorization
inferredcites →https://doi.org/10.1109/icme.2004.1394304openalex-snapshot
2004 IEEE International Conference on Multimedia and Expo
inferredcites →https://doi.org/10.1109/jcai15434.2009openalex-snapshot
2009 International Joint Conference on Artificial Intelligence
inferredcites →https://doi.org/10.1109/tip.2016.2531289openalex-snapshot
Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation
inferredcites →https://doi.org/10.1109/tip.2016.2599102openalex-snapshot
Coarse-to-Fine Description for Fine-Grained Visual Categorization
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.2018.2869721openalex-snapshot
Extracting Privileged Information for Enhancing Classifier Learning
inferredcites →https://doi.org/10.1109/tkde.2019.2903036openalex-snapshot
Towards Automatic Construction of Diverse, High-Quality Image Datasets
inferredcites →https://doi.org/10.1109/tmm.2017.2684626openalex-snapshot
Exploiting Web Images for Dataset Construction: A Domain Robust Approach
inferredcites →https://doi.org/10.1109/tmm.2018.2847248openalex-snapshot
Extracting Multiple Visual Senses for Web Learning
inferredcites →https://doi.org/10.1109/tnnls.2020.2966644openalex-snapshot
Exploiting Web Images for Multi-Output Classification: From Category to Subcategories
inferredcites →https://doi.org/10.1109/tpami.2016.2637331openalex-snapshot
Webly-Supervised Fine-Grained Visual Categorization via Deep Domain Adaptation
inferredcites →https://doi.org/10.1109/tpami.2017.2705122openalex-snapshot
Personalized Age Progression with Bi-Level Aging Dictionary Learning
inferredcites →https://doi.org/10.1109/tpami.2018.2852750openalex-snapshot
Deep Collaborative Embedding for Social Image Understanding
inferredcites →https://doi.org/10.1109/tpami.2019.2942030openalex-snapshot
Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition
inferredcites →https://doi.org/10.48550/arxiv.1306.5151openalex-snapshot
Fine-Grained Visual Classification of Aircraft
inferredcites →https://doi.org/10.48550/arxiv.1412.6596openalex-snapshot
Training Deep Neural Networks on Noisy Labels with Bootstrapping
inferredcites →https://doi.org/10.48550/arxiv.1802.05300openalex-snapshot
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
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.

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A Novel CNN Architecture for Real-Time Point Cloud Recognition in Road Environment
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Multi-model Network for Fine-Grained Cross-Media Retrieval
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Selective Learning from External Data for CT Image Segmentation
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Few-Shot Object Detection via Understanding Convolution and Attention
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Feature Difference Enhancement Fusion for Remote Sensing Image Change Detection
inferred← citeshttps://doi.org/10.1007/978-3-031-18913-5_7openalex-snapshot
Unsupervised Pre-training for 3D Object Detection with Transformer
inferred← citeshttps://doi.org/10.1007/s10994-023-06313-0openalex-snapshot
ProtoSimi: label correction for fine-grained visual categorization
inferred← citeshttps://doi.org/10.1007/s11063-023-11425-8openalex-snapshot
CTLC: A Co-Training-Based Label Correction Method for Webly-Supervised Learning
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Group benefits instance for data purification
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Jo-SRC: A Contrastive Approach for Combating Noisy Labels
inferred← citeshttps://doi.org/10.1109/iccv48922.2021.01043openalex-snapshot
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
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ADA-VIT: Attention-Guided Data Augmentation for Vision Transformers
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Improving Automatic Target Recognition With Infrared Imagery Using Vision Transformers and Focused Data Augmentation
inferred← citeshttps://doi.org/10.1109/mmsp55362.2022.9948793openalex-snapshot
Denoising Labels: A Noise-robust Method for Webly-supervised Fine-grained Visual Classification
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Multi-scale Self-attention-based Few-shot Object Detection for Remote Sensing Images
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Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Open-Set Noise and Utilizing Hard Examples
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Co-LDL: A Co-Training-Based Label Distribution Learning Method for Tackling Label Noise
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Exploiting Web Images for Fine-Grained Visual Recognition via Dynamic Loss Correction and Global Sample Selection
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Robust Learning From Noisy Web Images Via Data Purification for Fine-Grained Recognition
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Guided by Meta-Set: A Data-Driven Method for Fine-Grained Visual Recognition
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Jo-SNC: Combating Noisy Labels Through Fostering Self- and Neighbor-Consistency
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A Deformable Convolutional Neural Network with Oriented Response for Fine-Grained Visual Classification
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Extracting Useful Knowledge from Noisy Web Images via Data Purification for Fine-Grained Recognition
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Guided data augmentation for improved semi-supervised image classification in low data regime.
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Webly-Supervised Fine-Grained Recognition with Partial Label Learning
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Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
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