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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Chen, J. (2020) · International Conference on Computing and Artificial Intelligence (ICCAI) · doi:10.1145/3404555.3404622 · Cited by 4
“To solve this problem, in 2016, Zhicheng Yan et al. [25] introduced the hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy.”
Method
Toward Robotic Cognition by Means of Decision Tree of Deep Neural Networks Applied in a Humanoid Robot
Silva, I. J., Vilão, C. O., Costa, A. H. R., & Bianchi, R. A. C. (2021) · Journal of Control Automation and Electrical Systems · doi:10.1007/s40313-021-00719-8 · Cited by 3
“…the hierarchical approach to large document collections, employing stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy; and Yan et al. (2015) decomposed the image classiﬁcation problem by embedding deep CNNs into a category hierarchy.”
Background
A Boosting-Based Deep Distance Metric Learning Method
Li, Z. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2665843 · Cited by 3
“From [23, 24], we learn a two-level category hierarchy by using coarse and fine classifiers.”
Method
An iterative dynamic ensemble weighting approach for deep learning applications
Gültekin, T., & Uğur, A. (2017) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap.2017.8090318 · Cited by 3
“For another study [4], focused CNNs for classification are created meaning that one component is responsible for coarse classification and other one is used for fine classification.”
Facial Landmark Localization in the Wild by Backbone-Branches Representation Learning
Liu, L., Li, G., Xie, Y., Lin, L., & Yu, Y. (2018) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2018.8499059 · Cited by 3
“This motivates us to develop a cascaded coarse-to-fine process to localize landmarks progressively, in a spirit similar to the hierarchical deep networks in [34] for image classification.”
MethodSelf-citation
From multi-label learning to cross-domain transfer: a model-agnostic approach
Read, J. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04841-9 · Cited by 3
Learning Deep Representations for Scene Labeling with Semantic Context Guided SupervisionPreprint
Wang, Z., Li, H., Ouyang, W., & Wang, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02493 · Cited by 3
“Yan et al. (2015) built label hierarchies by grouping confusing fine categories into the same coarse categories.”
Contemplating Visual Emotions: Understanding and Overcoming Dataset BiasPreprint
Panda, R., Zhang, J., Li, H., Lee, J., Lu, X., & Roy–Chowdhury, A. K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.02212 · Cited by 3
“Category hierarchies have been successfully lever-aged in several recognition tasks: image classiﬁcation [61,58,20,34,3,8], object detection [9,39], image annotation [52], and concept learning [24] (see [46] for an overview).”
BackgroundMethod
Dysplasia grading of colorectal polyps through CNN analysis of WSIPreprint
Perlo, D., Tartaglione, E., Bertero, L., Cassoni, P., & Grangetto, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.05498 · Cited by 3
“Towards this end, we will follow a hierarchical-like classification approach [26,27], grouping the adenoma classes into high grade (HG) and low grade (LG) dysplasia.”
Method
Overview of Usable Privacy Research: Major Themes and Research Directions
Fischer‐Hübner, S., & Karegar, F. (2024) · Synthesis lectures on information security, privacy, and trust · doi:10.1007/978-3-031-54158-2_3 · Cited by 3
Drone-captured vehicle re-identification via perspective mask segmentation and hard sample learning
Liu, C., Baoqi, X., Li, S., & Faliang, C. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19260-2 · Cited by 2
Advancing precision agriculture: domain-specific augmentations and robustness testing for convolutional neural networks in precision spraying evaluation
Rogers, H., de la Iglesia, B., Zebin, T., Cielniak, G., & Magri, B. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10142-0 · Cited by 6
Holo4Care: a MR framework for assisting in activities of daily living by context-aware action recognition
Benavent-Lledó, M., Mulero-Pérez, D., García‐Rodríguez, J., Martínez-Martín, E., & Vizcaya-Moreno, M. F. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20107-z · Cited by 2
Regulating Model Reliance on Non-robust Features by Smoothing Input Marginal Density
Yang, P., Akhtar, N., Shah, M. A., & Mian, A. S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72998-0_19 · Cited by 4
End-to-End Latency Optimization for Resilient Distributed Convolutional Neural Network Inference in Resource-Constrained Unmanned Aerial Vehicle Swarms
Kim, J.-H., Seon, J., Kim, S. H., Lee, S., Kim, J., Hwang, B., Sun, Y. G., & Kim, J. (2024) · Applied Sciences · doi:10.3390/app142310832 · Cited by 4
C2F-CHART: A Curriculum Learning Approach to Chart Classification
Shaheen, N., Elsharnouby, T., & Torki, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78107-0_24 · Cited by 3
A 3D decoupling Alzheimer’s disease prediction network based on structural MRI
Wei, S., Yang, W., Wang, E., Wang, S., & Li, Y. (2025) · Health Information Science and Systems · doi:10.1007/s13755-024-00333-3 · Cited by 3

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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