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Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification

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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-01261-8_13openalex-snapshot
SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network
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/10.1007/978-3-319-46487-9_19openalex-snapshot
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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ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/cvpr.2015.7298775openalex-snapshot
Deep LAC: Deep localization, alignment and classification for fine-grained recognition
inferredcites →https://doi.org/10.1109/cvpr.2015.7298885openalex-snapshot
Learning from massive noisy labeled data for image classification
inferredcites →https://doi.org/10.1109/cvpr.2015.7298894openalex-snapshot
Visual recognition by learning from web data: A weakly supervised domain generalization approach
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.2018.00010openalex-snapshot
Finding Tiny Faces in the Wild with Generative Adversarial Network
inferredcites →https://doi.org/10.1109/cvpr.2018.00432openalex-snapshot
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
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.2018.00906openalex-snapshot
Iterative Learning with Open-set Noisy Labels
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.2017.557openalex-snapshot
Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition
inferredcites →https://doi.org/10.1109/iccvw.2013.77openalex-snapshot
3D Object Representations for Fine-Grained Categorization
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/tpami.2010.133openalex-snapshot
Harvesting Image Databases from the Web
inferredcites →https://doi.org/10.1109/tpami.2016.2637331openalex-snapshot
Webly-Supervised Fine-Grained Visual Categorization via Deep Domain Adaptation
inferredcites →https://doi.org/10.1167/17.10.296openalex-snapshot
Places: An Image Database for Deep Scene Understanding
inferredcites →https://doi.org/10.48550/arxiv.1306.5151openalex-snapshot
Fine-Grained Visual Classification of Aircraft
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.1412.6596openalex-snapshot
Training Deep Neural Networks on Noisy Labels with Bootstrapping
inferredcites →https://doi.org/10.48550/arxiv.1503.02531openalex-snapshot
Distilling the Knowledge in a Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1706.02613openalex-snapshot
Decoupling "when to update" from "how to update"
inferredcites →https://doi.org/10.48550/arxiv.1706.05394openalex-snapshot
A Closer Look at Memorization in Deep Networks
inferredcites →https://doi.org/10.48550/arxiv.1712.05055openalex-snapshot
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
inferredcites →https://doi.org/10.5555/3327757.3327944openalex-snapshot
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
inferredcites →https://doi.org/10.57702/i9oeewf6openalex-snapshot
Caltech-UCSD Birds-200-2011 Dataset
inferredcites →https://doi.org/10.57702/uwcm8hhfopenalex-snapshot
Enhancing the reliability of out-of-distribution image detection in neural networks

What points at this work

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

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CTLC: A Co-Training-Based Label Correction Method for Webly-Supervised Learning
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Rethinking Attention Mechanism: Channel Re-attention and Spatial Multi-region Attention for Fine-grained Visual Classification
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Meta label associated loss for fine-grained visual recognition
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Group benefits instance for data purification
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