The DOI matches its Crossref record; 37 of the 44 references it cites were found in a registry or an index.
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition
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%.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Convolutional neural networks (CNNs or ConvNets) are a class of deep feed-forward neural networks whose main purpose is to extract spatial patterns from visual input signals [20, 22].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [22]
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10578-9_23 · Cited by 3,034
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · cited 1×
“Specifically, we choose product quantization [14] to compress the parameter matrix W ∈ Rm×n.”
Method
Flagged first, then by citations
7 of the 44 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- jia, 2013, Caffe An Open Source Convolutional Architecture for Fast Feature Embedding
- jia, 2013, Visual concept learning: Combining machine vision and bayesian generalization on concept hierarchies, NIPS
- lee, 2014, Deeply-supervised nets arXiv preprint arXiv 1409 5185
- lin, 2013, Network in network, CoRR
Cited by
Sorted by citations
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
Deep learning for visual understanding: A review
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504
Contrastive Learning for Unpaired Image-to-Image Translation
Park, T., Efros, A. A., Zhang, R., & Zhu, J.-Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_19 · Cited by 1,663
The iNaturalist Species Classification and Detection Dataset
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., & Belongie, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00914 · Cited by 1,451
“This direction would encourage new error measures that incorporate taxonomic rank [25, 47].”
BackgroundDynamic Neural Networks: A Survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117837 · Cited by 848
“For instance, a small CNN is first executed to classify each sample into coarse categories, and specific sub-networks are conditionally activated based on the coarse predictions [103].”
BackgroundDeep Contrast Learning for Salient Object Detection
Li, G., & Yu, Y. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.58 · Cited by 803
“Recently, machine learning and artificial intelligence have been revolutionized by deep convolutional neural networks, which have set new state of the art on a number of visual recognition tasks, including image classification [25], object detection [16], scene classification [48] and scene parsing [13], closing the g…”
BackgroundSelf-citationNguyen, D., Hong, H. G., Kim, K., & Park, K. R. (2017) · Sensors · doi:10.3390/s17030605 · Cited by 803
Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
Zhang, H., Chang, H., Ma, B., Wang, N., & Chen, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_16 · Cited by 735
Self-citationWavelet Convolutions for Large Receptive Fields
Finder, S. E., Amoyal, R., Treister, E., & Freifeld, O. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72949-2_21 · Cited by 634
In-Domain GAN Inversion for Real Image Editing
Zhu, J., Shen, Y., Zhao, D., & Zhou, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58520-4_35 · Cited by 538
Beyond one-hot encoding: Lower dimensional target embedding
Rodríguez, P., Bautista, M. Á., Gonzàlez, J., & Escalera, S. (2018) · Image and Vision Computing · doi:10.1016/j.imavis.2018.04.004 · Cited by 485
“In the field of large-scale recognition, hierarchical approaches such as using tree-based priors [38], label relational graphs [11], CNN hierarchies [46], and HD-CNNs [47] have been proposed.”
MethodBayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456
Lu, Y., Kumar, A., Zhai, S., Cheng, Y., Javidi, T., & Feris, R. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.126 · Cited by 386
“We note that other techniques such as HD-CNN [46] and Network of Experts [1] also group related classes to perform hierarchical classification, but these methods are not applicable for the multi-label setting (where labels are not mutually exclusive).”
MethodXu, Y., Quan, R., Xu, W., Huang, Y., Chen, X., & Liu, F. (2024) · Bioengineering · doi:10.3390/bioengineering11101034 · Cited by 393
Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation
Wang, H., Shen, T., Zhāng, W., Duan, L., & Mei, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_38 · Cited by 330
The Plant Pathology Challenge 2020 data set to classify foliar disease of apples
Thapa, R., Zhang, K., Snavely, N., Belongie, S., & Khan, A. (2020) · Applications in Plant Sciences · doi:10.1002/aps3.11390 · Cited by 322
Research on image classification model based on deep convolution neural network
Xin, M., & Wang, Y. (2019) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-019-0417-8 · Cited by 284
Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning
Roy, D., Panda, P., & Roy, K. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.09.010 · Cited by 257
“Ima-geNet [6] contains 1.2 million images with 1000 categories), supervised learning has become the leading paradigm in training DCNNs for image recognition.”
BackgroundInfluentialYu, C., Han, R., Song, M., Liu, C., & Chang, C. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2983224 · Cited by 261
Qian, X., Pei, J., Zheng, H., Xie, X., Lin, Y., Zhang, H., Han, C., Gao, X., Zhang, H., Zheng, W., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00711-2 · Cited by 257
Self-citationDivide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN
Sam, D. B., Sajjan, N. N., Babu, R. V., & Srinivasan, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00381 · Cited by 250
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning
Ge, W., & Yu, Y. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.9 · Cited by 226
Self-citationDANet: Divergent Activation for Weakly Supervised Object Localization
Xue, H., Liu, C., Wan, F., Jiao, J., Ji, X., & Ye, Q. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00669 · Cited by 225
“Our research is also related to category hierarchy of images, which has been exploited for fine-grained image recognition [34, 32, 14, 30, 2].”
BackgroundPham, H., Le, T., Tran, D. Q., Ngo, D. T., & Nguyen, H. Q. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.03.127 · Cited by 234
Hierarchical convolutional neural networks for fashion image classification
Seo, Y., & Shin, K.-S. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.022 · Cited by 209
Do Convolutional Neural Networks Learn Class Hierarchy?
Alsallakh, B., Jourabloo, A., Ye, M., Liu, X., & Ren, L. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2744683 · Cited by 214
“Our results show more significant improvement on the ILSVRC dataset than HD-CNN, a recently proposed approach to implement hierarchical CNNs [72].”
ResultStochastic Dilated Residual Ghost Model for Breast Cancer Detection
Kashyap, R. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00739-z · Cited by 199
Deep Hierarchical Semantic Segmentation
Li, L., Zhou, T., Wang, W., Li, J., & Yang, Y. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00131 · Cited by 212
“In the field of computer vision, exiting efforts for class taxonomy aware image classification can be broadly divided into three groups [7]: i) Label-embedding methods [2, 6, 24, 88] that embed class labels to vectors whose relative locations represent semantic relationships; ii) Hierarchical losses [7,9,21,78,103] wh…”
BackgroundTreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation
Yue, K., Yang, L., Li, R., Hu, W., Zhang, F., & Li, W. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.07.007 · Cited by 197
Whale counting in satellite and aerial images with deep learning
Guirado, E., Tabik, S., Rivas, M. L., Alcaraz‐Segura, D., & Herrera, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-50795-9 · Cited by 198
“…and sea conditions in the training datasets, by building hierarchical training datasets with different behaviour across different species 67 , by using more spectral bands and temporal information 68 , and by artificially increasing the spatial resolution of the images through rendering…”
BackgroundCross-Modal Weighting Network for RGB-D Salient Object Detection
Li, G., Liu, Z., Ye, L., Wang, Y., & Ling, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58520-4_39 · Cited by 182
Li, D., Cong, A., & Guo, S. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.01.017 · Cited by 180
Talking-Head Generation with Rhythmic Head Motion
Chen, L., Cui, G., Liu, C., Li, Z., Kou, Z., Xu, Y., & Xu, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_3 · Cited by 179
Crop mapping from image time series: Deep learning with multi-scale label hierarchies
Türkoglu, M. Ö., D’Aronco, S., Perich, G., Liebisch, F., Streit, C., Konrad Schindler, B., & Wegner, J. D. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112603 · Cited by 194
Singh, P., Chaudhury, S., & Panigrahi, B. K. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100863 · Cited by 179
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space
Huang, R., & Li, Y. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00860 · Cited by 183
“formance [39]. Some works aim to learn a label tree structure when taxonomy is unavailable [2,9,31]. As a typical hierarchy, groupbased learning has been widely adopted in image classifi- cation tasks [18,1,50,48,12]. Recently, a group softmax classifier is proposed to tackle the problem of long-tail object detection, w…”
BackgroundProgressively Guided Alternate Refinement Network for RGB-D Salient Object Detection
Chen, S., & Fu, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58598-3_31 · Cited by 163
Hijjawi, U., Lakshminarayana, S., Xu, T., Fierro, G. P. M., & Rahman, M. (2023) · Solar Energy · doi:10.1016/j.solener.2023.112186 · Cited by 186
VegFru: A Domain-Specific Dataset for Fine-Grained Visual Categorization
Hou, S., Feng, Y., & Wang, Z. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.66 · Cited by 159
Machine learning techniques for protein function prediction
Bonetta, R., & Valentino, G. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25832 · Cited by 161
Feedback Attention-Based Dense CNN for Hyperspectral Image Classification
Yu, C., Han, R., Song, M., Liu, C., & Chang, C. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3058549 · Cited by 160
CNN-RNN: a large-scale hierarchical image classification framework
Guo, Y., Liu, Y., Bakker, E. M., Guo, Y., & Lew, M. S. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5443-x · Cited by 154
Deep Spiking Neural Network: Energy Efficiency Through Time Based Coding
Han, B., & Roy, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58607-2_23 · Cited by 149
Making Better Mistakes: Leveraging Class Hierarchies With Deep Networks
Bertinetto, L., Mueller, R., Τερτίκας, Κ., Samangooei, S., & Lord, N. A. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01252 · Cited by 166
Learning Relaxed Deep Supervision for Better Edge Detection
Liu, Y., & Lew, M. S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.32 · Cited by 144
Delving deeper into convolutional neural networks for camera relocalization
Wu, J., Ma, L., & Hu, X. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989663 · Cited by 148
“HDCNNs are presented which consists of both coarse component trained over all classes and fine components trained over subsets of classes [18].”
BackgroundA Survey on Large-Scale Machine Learning
Wang, M., Fu, W., He, X., Hao, S., & Wu, X. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3015777 · Cited by 158
“For example, inspired by the coarse-to-fine categorization of visual scenes in scene-selective cortex [147], hierarchical convolutional networks are developed for image classification [213].”
BackgroundIntrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network
Mendonça, R. V., Teodoro, A. A. M., Rosa, R. L., Saadi, M., Carrillo, D., Nardelli, P. H. J., & Rodríguez, D. Z. (2021) · IEEE Access · doi:10.1109/access.2021.3074664 · Cited by 143
Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery
Tripathi, M. K., Nath, A., Singh, T. P., Ethayathulla, A. S., & Kaur, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10256-w · Cited by 151
First demonstration of in-memory computing crossbar using multi-level Cell FeFET
Soliman, T., Chatterjee, S., Laleni, N., Müller, F., Kirchner, T., Wehn, N., Kämpfe, T., Chauhan, Y. S., & Amrouch, H. (2023) · Nature Communications · doi:10.1038/s41467-023-42110-y · Cited by 148
Harrison, A. P., Xu, Z., George, K., Lu, L., Summers, R. M., & Mollura, D. J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_71 · Cited by 129
Interpretable Image Recognition with Hierarchical Prototypes
Hase, P., Chen, C., Li, O., & Rudin, C. D. (2019) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v7i1.5265 · Cited by 131
A Neural Architecture for Automated ICD Coding
Xie, P., & Xing, E. P. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1098 · Cited by 130
Pose-Invariant Face Alignment with a Single CNN
Jourabloo, A., Ye, M., Liu, X., & Ren, L. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.347 · Cited by 123
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
Kang, L., Riba, P., Wang, Y., Rusiñol, M., Fornés, A., & Villegas, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58592-1_17 · Cited by 138
Learning Visual Representations with Caption Annotations
Sarıyıldız, M. B., Pérez, J., & Larlus, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58598-3_10 · Cited by 128
Do Not Mask What You Do Not Need to Mask: A Parser-Free Virtual Try-On
Issenhuth, T., Mary, J., & Calauzènes, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58565-5_37 · Cited by 120
Detecting Human-Object Interactions with Action Co-occurrence Priors
Kim, D.-J., Sun, X. N., Choi, J., Lin, S. C.-F., & Kweon, I. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_43 · Cited by 119
Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks
Lee, S. H., Chan, C. S., & Remagnino, P. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2836321 · Cited by 118
“shared layer is inspired by: (1) the work of [17] and [59], who demonstrated that bottom layers in deep networks respond to low-level features, such as corners and edges, in turn crucial to the classification of any high level features, and, (2) the fact that such layers help reducing the number of training parameters.”
BackgroundSOLAR: Second-Order Loss and Attention for Image Retrieval
Ng, T., Balntas, V., Tian, Y., & Mikolajczyk, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_16 · Cited by 116
B-CNN: Branch Convolutional Neural Network for Hierarchical ClassificationPreprint
Zhu, X., & Bain, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.09890 · Cited by 172
“[32] have made a significant contribution in discovering the possibility of combing the label tree and the hierarchical nature of CNN.”
BackgroundGraphology based handwritten character analysis for human behaviour identification
Ghosh, S., Shivakumara, P., Roy, P., Pal, U., & Lü, T. (2020) · CAAI Transactions on Intelligence Technology · doi:10.1049/trit.2019.0051 · Cited by 113
Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics
Byeon, Y.-H., Pan, S., & Kwak, K.-C. (2019) · Sensors · doi:10.3390/s19040935 · Cited by 112
Han, L., Yu, C., Xiao, K., & Zhao, X. (2019) · Sensors · doi:10.3390/s19091960 · Cited by 110
Survey: Image mixing and deleting for data augmentation
Naveed, H., Anwar, S., Hayat, M., Javed, K., & Mian, A. S. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107791 · Cited by 138
Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery
Wang, G., Fan, B., Xiang, S., & Pan, C. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2705419 · Cited by 108
“large success in natural scene classification [9]–[11] and object recognition [12].”
BackgroundA Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
Liu, C., Dong, Y., Xiang, W., Yang, X. H., Su, H., Zhu, J., Chen, Y., He, Y., Xue, H., & Zheng, S. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02196-3 · Cited by 162
Differentiable Automatic Data Augmentation
Li, Y., Hu, G., Wang, Y., Hospedales, T. M., Robertson, N. M., & Yang, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58542-6_35 · Cited by 171
Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation
Yang, J., An, W., Wang, S., Zhu, X., Yan, C., & Huang, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58583-9_29 · Cited by 105
Face attribute prediction using off-the-shelf CNN features
Zhong, Y., Sullivan, J., & Li, H. (2016) · International Conference on Biometrics · doi:10.1109/icb.2016.7550092 · Cited by 101
“ifiers directly based on attributes [10], but also efficiently construct highly flexible large-scale hierarchical datasets, which can further benefit image classification and attribute-to-image generation [23,22]. The general process of predicting face attributes is to construct face representations and train domain classi…”
BackgroundHierarchy-Based Image Embeddings for Semantic Image Retrieval
Barz, B., & Denzler, J. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00073 · Cited by 106
“HD-CNN [36] follows an end-to-end learning approach by dividing a CNN into a coarse and several fine classifiers based on two levels of a class hierarchy, fusing predictions at the end.”
MethodOptimizing 5’UTRs for mRNA-delivered gene editing using deep learning
Castillo-Hair, S., Fedak, S., Wang, B., Linder, J., Havens, K., Certo, M., & Seelig, G. (2024) · Nature Communications · doi:10.1038/s41467-024-49508-2 · Cited by 98
Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework
Zhang, S., Xu, R., Xiong, C., & Ramaiah, C. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01616 · Cited by 102
“[47] designed a deep network branch on side of a general network, and strategically learn super-classes at each hierarchical level.”
BackgroundNetwork of Experts for Large-Scale Image Categorization
Ahmed, K., Baig, M. H., & Torresani, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_32 · Cited by 100
“The architectures in [17, 38] route the input image to only a subset of experts, those deemed more competent in its categorization.”
BackgroundMethodInfluentialWebly-Supervised Video Recognition by Mutually Voting for Relevant Web Images and Web Video Frames
Gan, C., Sun, C., Duan, L., & Gong, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_52 · Cited by 96
“Motivated by the success of convolutional neural networks on image recognition tasks [21,39,30,45], there are also several attempts to apply deep learning techniques for video recognition.”
Gao, Z., Pan, X., Shao, J., Jiang, X., Su, Z., Jin, K., & Ye, J. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2022-321472 · Cited by 93
DualNet: Learn Complementary Features for Image Recognition
Hou, S., Liu, X., & Wang, Z. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.62 · Cited by 86
“∗-with cropping strategy and 10 view testing [35].”
BackgroundMethodInfluentialTargeted Attack for Deep Hashing Based Retrieval
Bai, J., Chen, B., Li, Y., Wu, D., Guo, W., Xia, S., & Yang, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_36 · Cited by 90
Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias
Panda, R., Zhang, J., Li, H., Lee, J., Lu, X., & Roy–Chowdhury, A. K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_36 · Cited by 85
Effective Combination of DenseNet and BiLSTM for Keyword Spotting
Mengjun, Z., & Xiao, N. (2019) · IEEE Access · doi:10.1109/access.2019.2891838 · Cited by 85
3D geological structure inversion from Noddy-generated magnetic data using deep learning methods
Guo, J., Li, Y., Jessell, M., Giraud, J., Li, C., Wu, L. X., Li, F., & Liu, S. (2021) · Computers & Geosciences · doi:10.1016/j.cageo.2021.104701 · Cited by 78
Deep Decision Network for Multi-class Image Classification
Murthy, V. N., Singh, V. K., Chen, T., Manmatha, R., & Comaniciu, D. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.246 · Cited by 78
“Fourthly, DDN uses multiple decision layers capable of making early decisions as opposed to using one probabilistic averaging layer and finally our method results on CIFAR-100 is better even without any data-augmentation and 10 average testing [12] tricks as used in [32].”
MethodResultInfluentialReparameterizing Convolutions for Incremental Multi-Task Learning Without Task Interference
Kanakis, M., Brüggemann, D., Saha, S., Georgoulis, S., Obukhov, A., & Gool, L. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58565-5_41 · Cited by 79
Guha Roy, A., Ren, J., Azizi, S., Loh, A., Natarajan, V., Mustafa, B., Pawlowski, N., Freyberg, J., Liu, Y., Beaver, Z., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102274 · Cited by 77
“Note that Yan et al. (2015) also incorporate the hierarchy among image classes into the model but in a different way: independent fine grained classification heads are constructed for each high level class.”
BackgroundHierarchical Novelty Detection for Visual Object Recognition
Lee, K., Lee, K., Min, K., Zhang, Y., Shin, J., & Lee, H. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00114 · Cited by 80
Hard Mixtures of Experts for Large Scale Weakly Supervised Vision
Gross, S., Ranzato, M., & Szlam, A. D. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.540 · Cited by 75
Shahabi, M. S., Shalbaf, A., & Maghsoudi, A. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.06.006 · Cited by 79
An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification
Wang, X., Liesaputra, V., Liu, Z., Wáng, Y., & Huang, Z. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102738 · Cited by 99
HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition
Jianping Fan, B., Zhao, T., Kuang, Z., Zheng, Y., Zhang, J., Yu, J., & Peng, J. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2667405 · Cited by 76
Su, K., Liu, J., & Xiong, H. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107386 · Cited by 74
Two-layer Ensemble of Deep Learning Models for Medical Image Segmentation
Dang, T., Nguyen, T. T., McCall, J., Elyan, E., & Moreno‐García, C. F. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10257-5 · Cited by 82
Tomato maturity recognition with convolutional transformers
Khan, A. A., Hassan, T., Shafay, M., Fahmy, I., Werghi, N., Mudigansalage, S., & Hussain, I. (2023) · Scientific Reports · doi:10.1038/s41598-023-50129-w · Cited by 74
Circumventing Outliers of AutoAugment with Knowledge Distillation
Wei, L., Xiao, A., Xie, L., Zhang, X., Chen, X., & Tian, Q. C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_36 · Cited by 68
Learning to Make Better Mistakes
Wu, H., Merler, M., Uceda‐Sosa, R., & Smith, J. R. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2967205 · Cited by 65
Deep Learning Architectures for Accurate Millimeter Wave Positioning in 5G
Gante, J., Falcão, G., & Sousa, L. A. (2019) · Neural Processing Letters · doi:10.1007/s11063-019-10073-1 · Cited by 63
“This implementation of the hierarchy concept was based on the work in [29], where the prediction outcome of a coarse model may trigger specialized fine-grain models, which help to handle harder input data.”
BackgroundMethodInfluentialLearning More Universal Representations for Transfer-Learning
Tamaazousti, Y., Borgne, H. L., Hudelot, C., Seddik, M. E. A., & Tamaazousti, M. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913857 · Cited by 66
Knowledge Transfer via Dense Cross-Layer Mutual-Distillation
Yao, A., & Sun, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_18 · Cited by 61
Decision Fusion Networks for Image Classification
Tang, K., Ma, Y., Miao, D., Song, P., Gu, Z., Tian, Z., & Wang, W. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3196129 · Cited by 62
“Yan et al. [41] proposed a two-level hierarchical CNN with the first layer separating easy classes using a coarse category classifier and the second layer handling difficult classes.”
Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation
Chen, L., Wang, W., Miao, J., & Yang, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01484 · Cited by 67
Journey Towards Tiny Perceptual Super-Resolution
Lee, R., Dudziak, Ł., Abdelfattah, M. S., Venieris, S. I., Kim, H., Wen, H., & Lane, N. D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58574-7_6 · Cited by 58
Metadata
- 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)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2740-2748
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
