Cited by 936Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

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HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

Yan, Zhicheng, Zhang, Hao, Piramuthu, Robinson, Jagadeesh, Vignesh, DeCoste, Dennis, Di, Wei, Yu, Yizhou·2015·2015 IEEE International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv.2015.314

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Abstract

In image classification, visual separability between different object categories is highly uneven, and some categories are more difficult to distinguish than others. Such difficult categories demand more dedicated classifiers. However, existing deep convolutional neural networks (CNN) are trained as flat N-way classifiers, and few efforts have been made to leverage the hierarchical structure of categories. In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a two-level category hierarchy. An HD-CNN separates easy classes using a coarse category classifier while distinguishing difficult classes using fine category classifiers. During HDCNN training, component-wise pretraining is followed by global fine-tuning with a multinomial logistic loss regularized by a coarse category consistency term. In addition, conditional executions of fine category classifiers and layer parameter compression make HD-CNNs scalable for largescale visual recognition. We achieve state-of-the-art results on both CIFAR100 and large-scale ImageNet 1000-class benchmark datasets. In our experiments, we build up three different two-level HD-CNNs, and they lower the top-1 error of the standard CNNs by 2:65%, 3:1%, and 1:1%.

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Federated Adversarial Learning for Robust Autonomous Landing Runway Detection
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CONDA: Condensed Deep Association Learning for Co-salient Object Detection
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Comparative Study of Computer Vision Models for Insect Pest Identification in Complex Backgrounds
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Background
Visualization of Salient Object With Saliency Maps Using Residual Neural Networks
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Method
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Background
Conclusions Drawn From Neural Network to Brain Alignment Depend Strongly on the Chosen Similarity MeasurePreprint
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State-of-the-art skin disease classification: a review of deep learning models
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“This is because they require only a small portion of the extensive training data volume to be cached during sequential scans (Yan et al. 2015).”

Authors: Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., DeCoste, D., Di, W., & Yu, Y.
Affiliations: University of Illinois Urbana-Champaign (US)
Carnegie Mellon University (US)
eBay (Ireland) (IE)
ORCID: Yan, Zhicheng
Piramuthu, Robinson
Yu, Yizhou
Venue: 2015 IEEE International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2015-12
Pages: 2740-2748
DOI: 10.1109/iccv.2015.314
OpenAlex: W2962860144
Cited by: 936
References: 44
Citations by year: 2026: 19 · 2025: 79 · 2024: 310 · 2023: 142 · 2022: 42 · 2021: 79 · 2020: 103 · 2019: 61 · 2018: 41 · 2017: 43 · 2016: 16 · 2015: 1
FWCI: 13.68
Type: conference paper
Language: en
Open access: closed
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords: coarse-to-fine classification, large-scale image classification

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HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

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