The DOI matches its Crossref record; 37 of the 40 references it cites were found in a registry or an index.
Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition
Abstract
Recognizing fine-grained categories (e.g., bird species) highly relies on discriminative part localization and part-based fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that part localization (e.g., head of a bird) and fine-grained feature learning (e.g., head shape) are mutually correlated. In this paper, we propose a novel part learning approach by a multi-attention convolutional neural network (MA-CNN), where part generation and feature learning can reinforce each other. MA-CNN consists of convolution, channel grouping and part classification sub-networks. The channel grouping network takes as input feature channels from convolutional layers, and generates multiple parts by clustering, weighting and pooling from spatially-correlated channels. The part classification network further classifies an image by each individual part, through which more discriminative fine-grained features can be learned. Two losses are proposed to guide the multi-task learning of channel grouping and part classification, which encourages MA-CNN to generate more discriminative parts from feature channels and learn better fine-grained features from parts in a mutual reinforced way. MA-CNN does not need bounding box/part annotation and can be trained end-to-end. We incorporate the learned parts from MA-CNN with part-CNN for recognition, and show the best performances on three challenging published fine-grained datasets, e.g., CUB-Birds, FGVC-Aircraft and Stanford-Cars.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
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 Deep Features for Discriminative Localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 5,299
Jaderberg, M., Simonyan, K., Zisserman, A., & Kavukcuoglu, K. (2015) · arXiv · doi:10.48550/arxiv.1506.02025 · Cited by 5,137
3D Object Representations for Fine-Grained Categorization
Krause, J., Stark, M. M., Deng, J., & Fei-Fei, L. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.77 · Cited by 3,666
Bilinear CNN Models for Fine-Grained Visual Recognition
Lin, T., RoyChowdhury, A., & Maji, S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.170 · Cited by 2,234
Flagged first, then by citations
3 of the 40 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- welinder, 2010, Caltech-Ucsd Birds 200, Technical Report
- khosla, 2011, Novel dataset for fine-grained image categorization, ICCV Workshop
Cited by
Sorted by citations
Items 101–200 of 1,000
Bu, S.-J., & Cho, S.-B. (2020) · Energies · doi:10.3390/en13184722 · Cited by 95
Look-Into-Object: Self-Supervised Structure Modeling for Object Recognition
Zhou, M., Bai, Y., Zhang, W., Zhao, T., & Mei, T. (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/cvpr42600.2020.01179 · Cited by 93
Self-citationHierarchical Pyramid Diverse Attention Networks for Face Recognition
Wang, Q., Wu, T., Zheng, H., & Guo, G. (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/cvpr42600.2020.00835 · Cited by 94
Self-citationLearning Semantically Enhanced Feature for Fine-Grained Image Classification
Luo, W., Zhang, H., Li, J., & Wei, X.-S. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3020227 · Cited by 90
Yuan, Z., Mou, L., Wang, Q., & Zhu, X. X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3173811 · Cited by 96
Pan, C., Tan, J., & Feng, D. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.027 · Cited by 91
Deep Learning for Fine-Grained Image Analysis: A SurveyPreprint
Wei, X.-S., Wu, J., & Cui, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.03069 · Cited by 98
Deep Saliency Hashing for Fine-Grained Retrieval
Jin, S., Yao, H., Sun, X., Zhou, S., Lei Zhang, A., & Hua, X. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2971105 · Cited by 87
Towards Interpretable Reinforcement Learning Using Attention Augmented AgentsPreprint
Mott, A., Zoran, D., Chrzanowski, M., Wierstra, D., & Rezende, D. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02500 · Cited by 87
Multiple Attention Network for Facial Expression Recognition
Gan, Y., Chen, J., Yang, Z., & Xu, L. (2020) · IEEE Access · doi:10.1109/access.2020.2963913 · Cited by 85
Zhuang, P., Wang, Y., & Qiao, Y. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240616 · Cited by 85
Huang, H., Zhang, J., Yu, L., Zhang, J., Wu, Q., & Xu, C. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3065693 · Cited by 84
Yan, C., Feng, X., Wick, C. D., Peters, A. J., & Li, G. (2020) · Polymer · doi:10.1016/j.polymer.2020.123351 · Cited by 86
Rymarczyk, D., Struski, Ł., Tabor, J., & Zieliński, B. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467245 · Cited by 83
Clip-aware expressive feature learning for video-based facial expression recognition
Liu, Y., Feng, C., Yuan, X., Zhou, L., Wang, W., Qin, J., & Luo, Z. (2022) · Information Sciences · doi:10.1016/j.ins.2022.03.062 · Cited by 83
Shen, J., Liu, N., Sun, H., Li, D., & Zhang, Y. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3346488 · Cited by 83
Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network
Hou, S., & Wang, Z. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018425 · Cited by 82
Part-Guided Relational Transformers for Fine-Grained Visual Recognition
Zhao, Y., Li, J., Chen, X., & Tian, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3126490 · Cited by 83
Mineral prospectivity mapping using attention-based convolutional neural network
Li, Q., Chen, G., & Luo, L. (2023) · Ore Geology Reviews · doi:10.1016/j.oregeorev.2023.105381 · Cited by 81
Singh, V. K., & Kolekar, M. H. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11158-7 · Cited by 77
AI-based acoustic leak detection in water distribution systems
Vanijjirattikhan, R., Khomsay, S., Kitbutrawat, N., Khomsay, K., Supakchukul, U., Udomsuk, S., Suwatthikul, J., Oumtrakul, N., & Anusart, K. (2022) · Results in Engineering · doi:10.1016/j.rineng.2022.100557 · Cited by 80
Development of an automatic monitoring system for rice light-trap pests based on machine vision
YAO, Q., FENG, J., TANG, J., XU, W.-G., ZHU, X.-H., YANG, B.-J., LÜ, J., XIE, Y.-Z., YAO, B., WU, S.-Z., et al. (2020) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(20)63168-9 · Cited by 76
A Survey of Vehicle Re-Identification Based on Deep Learning
Wang, H., Hou, J., & Chen, N. (2019) · IEEE Access · doi:10.1109/access.2019.2956172 · Cited by 77
Geo-Aware Networks for Fine-Grained Recognition
Chu, G., Potetz, B. R., Wang, W., Howard, A., Song, Y., Brücher, F., Leung, T., & Adam, H. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00033 · Cited by 76
Task-aware Part Mining Network for Few-Shot Learning
Wu, J., Zhang, T., Zhang, Y., & Wu, F. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00832 · Cited by 76
Action Recognition With Spatial-Temporal Discriminative Filter Banks
Martínez, B., Modolo, D., Xiong, Y., & Tighe, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00558 · Cited by 75
Food and Ingredient Joint Learning for Fine-Grained Recognition
Liu, C., Liang, Y., Xue, Y., Qian, X., & Fu, J. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3020079 · Cited by 75
Self-citationFeature Boosting, Suppression, and Diversification for Fine-Grained Visual Classification
Song, J., & Yang, R. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534004 · Cited by 76
Detection of Deepfake Videos Using Long-Distance Attention
Lu, W., Liu, L., Zhang, B., Luo, J., Zhao, X., Zhou, Y., & Huang, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3233063 · Cited by 78
Self-citationBi-Modal Progressive Mask Attention for Fine-Grained Recognition
Song, K., Wei, X.-S., Shu, X., Song, R., & Lu, J. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2996736 · Cited by 75
Huang, S., Wang, X., & Tao, D. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00066 · Cited by 74
AttU-NET: Attention U-Net for Brain Tumor Segmentation
Wang, S., Li, L., & Zhuang, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09002-8_27 · Cited by 74
Xie, W., Shen, L., & Zisserman, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_48 · Cited by 73
Progressive Co-Attention Network for Fine-Grained Visual Classification
Zhang, T. Y., Chang, D., Ma, Z., & Guo, J. H. (2021) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip53242.2021.9675376 · Cited by 73
Small sample image recognition using improved Convolutional Neural Network
Zhang, J., Shao, K., & Luo, X. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.07.011 · Cited by 74
Multi-Path Deep CNNs for Fine-Grained Car Recognition
Wang, H., Peng, J., Zhao, Y., & Fu, X. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3009162 · Cited by 71
Discriminative Feature Learning With Foreground Attention for Person Re-Identification
Zhou, S., Wang, J., Meng, D., Liang, Y., Gong, Y., & Zheng, N. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2908065 · Cited by 71
Fine-Grained Person Re-identification
Yin, J., Wu, A., & Zheng, W. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01259-0 · Cited by 70
Network traffic classification model based on attention mechanism and spatiotemporal features
Hu, F., Zhang, S., Lin, X., Wu, L., Liao, N., & Song, Y. (2023) · EURASIP Journal on Information Security · doi:10.1186/s13635-023-00141-4 · Cited by 76
Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule NetworksPreprint
Mobiny, A., Cicalese, P. A., Zare, S., Yuan, P., Abavisani, M., Wu, C. C., Ahuja, J., de Groot, P. M., & Nguyen, H. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.07407 · Cited by 70
Towards Robust Image Classification Using Sequential Attention Models
Zoran, D., Chrzanowski, M., Huang, P.-S., Gowal, S., Mott, A., & Kohli, P. (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/cvpr42600.2020.00950 · Cited by 69
Guo, Q., Wang, C., Xiao, D., & Huang, Q. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107060 · Cited by 68
Fine-Grained 3D Shape Classification With Hierarchical Part-View Attention
Liu, X., Han, Z., Liu, Y.-S., & Zwicker, M. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3048623 · Cited by 68
Interpretable visual transmission lines inspections using pseudo-prototypical part network
Singh, G., Stefenon, S. F., & Yow, K. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01390-6 · Cited by 68
ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval
Cui, Q., Jiang, Q.-Y., Wei, X.-S., Li, W.-J., & Yoshie, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_12 · Cited by 66
Multi-Scale Attention Deep Neural Network for Fast Accurate Object Detection
Song, K., Yang, H. S., & Yin, Z. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2875449 · Cited by 65
Scene Recognition With Prototype-Agnostic Scene Layout
Chen, G., Song, X., Zeng, H., & Jiang, S. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2986599 · Cited by 66
Bidirectional Attention-Recognition Model for Fine-Grained Object Classification
Liu, C., Xie, H., Zha, Z.-J., Yu, L., Chen, Z., & Zhang, Y. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2954747 · Cited by 63
Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification
Zhang, C., Yao, Y., Liu, H., Xie, G.-S., Shu, X., Zhou, T., Zhang, Z., Shen, F., & Tang, Z. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6973 · Cited by 61
FM-ECG: A fine-grained multi-label framework for ECG image classification
Du, N., Cao, Q., Li, Y., Liu, N., Zhong, E., Liu, Z., Shen, Y., & Chen, K. (2020) · Information Sciences · doi:10.1016/j.ins.2020.10.014 · Cited by 62
Fine-Grained Recognition With Learnable Semantic Data Augmentation
Pu, Y., Han, Y., Wang, Y., Feng, J., Deng, C., & Huang, G. M. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3364500 · Cited by 66
These do not Look Like Those: An Interpretable Deep Learning Model for Image Recognition
Singh, G., & Yow, K. (2021) · IEEE Access · doi:10.1109/access.2021.3064838 · Cited by 61
SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation
Zhao, X., Tang, F., Wang, X., & Xiao, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i7.28584 · Cited by 61
Saliency for fine-grained object recognition in domains with scarce training data
Figueroa-Flores, C., González-García, A., van de Weijer, J., & Raducanu, B. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.05.002 · Cited by 59
An Interpretable Deep Learning Model for Covid-19 Detection With Chest X-Ray Images
Singh, G., & Yow, K. (2021) · IEEE Access · doi:10.1109/access.2021.3087583 · Cited by 60
FET-FGVC: Feature-enhanced transformer for fine-grained visual classification
Chen, H., Zhang, H., Liu, C., An, J., Gao, Z., & Qiu, J. X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110265 · Cited by 57
Generative attention adversarial classification network for unsupervised domain adaptation
Chen, W., & Hu, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107440 · Cited by 58
MGML: Multigranularity Multilevel Feature Ensemble Network for Remote Sensing Scene Classification
Zhao, Q., Lyu, S., Li, Y., Ma, Y., & Chen, L. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3106391 · Cited by 56
CRS-CONT: A Well-Trained General Encoder for Facial Expression Analysis
Li, H., Wang, N., Yang, X., & Gao, X. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3186536 · Cited by 57
ADVISE: Symbolism and External Knowledge for Decoding Advertisements
Ye, K., & Kovashka, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_51 · Cited by 56
PETDet: Proposal Enhancement for Two-Stage Fine-Grained Object Detection
Li, W., Zhao, D., Yuan, B., Gao, Y., & Shi, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3343453 · Cited by 55
Huang, Y., Hechen, Z., Zhou, M., Li, Z. G., & Kwong, S. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3535818 · Cited by 55
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition
Nauta, M., Jutte, A., Provoost, J., & Seifert, C. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-93736-2_34 · Cited by 63
Sub-Region Localized Hashing for Fine-Grained Image Retrieval
Xiang, X., Zhang, Y., Lü, J., Li, Z., & Tang, J. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3131042 · Cited by 54
Convolutional Fine-Grained Classification With Self-Supervised Target Relation Regularization
Liu, K., Chen, K., & Jia, K. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3197931 · Cited by 54
Progressive Attention Networks for Visual Attribute PredictionPreprint
Seo, P. H., Lin, Z., Cohen, S. D., Shen, X., & Han, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.02393 · Cited by 56
Chen, C., Chen, Z., Jin, X., Li, L., Speier, W. F., & Arnold, C. W. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3095128 · Cited by 52
Zheng, Y., Qiu, B., Liu, S., Song, R., Yang, X., Wu, L., Chen, Z., Tuersun, A., Wang, W., Wang, W., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102805 · Cited by 51
From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach
Yan, C., Feng, X., & Li, G. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c20947 · Cited by 50
Glance and Focus Networks for Dynamic Visual Recognition
Huang, G. M., Wang, Y., Lv, K., Jiang, H., Huang, W., Qi, P., & Song, S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3196959 · Cited by 55
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition
Huynh, D., & Elhamifar, E. (2020) · neural information processing systems · Cited by 50
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition
Li, H., Zhang, X., Tian, Q., & Xiong, H. (2020) · IEEE International Conference on Visual Communications and Image Processing (VCIP) · doi:10.1109/vcip49819.2020.9301763 · Cited by 50
Zhou, J., Li, J., Wang, C., Wu, H., Zhao, C., & Wang, Q. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106101 · Cited by 50
Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution
Qiu, Z., Yang, H., Fu, J., & Fu, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19797-0_15 · Cited by 49
Self-citationFang, M., Tan, Z., Tang, Y., Chen, W., Huang, H., Dananjayan, S., He, Y., & Luo, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124833 · Cited by 50
Fine-Grained Video Categorization with Redundancy Reduction Attention
Zhu, C., Tan, X., Zhou, F., Liu, X., Yue, K., Ding, E., & Ma, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_9 · Cited by 49
Fine-Grained Image Retrieval via Piecewise Cross Entropy loss
Zeng, X., Zhang, Y., Wang, X., Chen, K., Li, D., & Yang, W. (2019) · Image and Vision Computing · doi:10.1016/j.imavis.2019.10.006 · Cited by 49
An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases
Quan, Q., Wang, J., & Liu, L. (2020) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00367-7 · Cited by 49
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments
Zhang, J., Inkawhich, N., Linderman, R., Chen, Y., & Li, H. H. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00549 · Cited by 56
Advances in deep learning approaches for image tagging
Fu, J., & Rui, Y. (2017) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2017.12 · Cited by 49
Self-citationWang, W., Cui, Y., Li, G., Jiang, C., & Deng, S. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05148-3 · Cited by 48
Competitive Inner-Imaging Squeeze and Excitation for Residual NetworkPreprint
Hu, Y., Wen, G., Luo, M., Dai, D., Ma, J., & Yu, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.08920 · Cited by 51
Embedding Pose Information for Multiview Vehicle Model Recognition
Yu, Y., Liu, H., Fu, Y., Jia, W., Yu, J., & Yan, Z. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3151116 · Cited by 48
Image Classification With Small Datasets: Overview and Benchmark
Brigato, L., Barz, B., Iocchi, L., & Denzler, J. (2022) · IEEE Access · doi:10.1109/access.2022.3172939 · Cited by 49
ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model
Li, Z., Gu, T., Li, B., Xu, W., He, X., & Hui, X. (2022) · Applied Sciences · doi:10.3390/app12189016 · Cited by 47
Dual Transformer With Multi-Grained Assembly for Fine-Grained Visual Classification
Ji, R., Li, J., Zhang, L., Liu, J., & Wu, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3248791 · Cited by 47
Weakly Supervised Fine-grained Image Classification via Correlation-guided Discriminative Learning
Wang, Z., Wang, S., Zhang, P., Li, H., Zhong, W., & Li, J. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350976 · Cited by 47
Ensemble Learning for Multidimensional Poverty Classification
Bakar, A. A., Hamdan, R., & Sani, N. S. (2020) · Sains Malaysiana · doi:10.17576/jsm-2020-4902-24 · Cited by 47
Deep Listwise Triplet Hashing for Fine-Grained Image Retrieval
Liang, Y., Pan, Y., Lai, H., Liu, W., & Yin, J. P. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3137653 · Cited by 47
Learning sequentially diversified representations for fine-grained categorization
Zhang, L., Huang, S., & Liu, W. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108219 · Cited by 46
Fine-Grained Hashing With Double Filtering
Chen, Z.-D., Luo, X., Wang, Y., Guo, S., & Xu, X.-S. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3145159 · Cited by 46
SEMICON: A Learning-to-Hash Solution for Large-Scale Fine-Grained Image Retrieval
Shen, Y., Sun, X., Wei, X.-S., Jiang, Q.-Y., & Yang, J. W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19781-9_31 · Cited by 46
Patchy Image Structure Classification Using Multi-Orientation Region Transform
Yu, X., Zhao, Y. S., Gao, Y., Xiong, S., & Yuan, X. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6968 · Cited by 47
Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery
Rodríguez, P., Gonfaus, J. M., Cucurull, G., Roca, F. X., & Gonzàlez, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_22 · Cited by 45
Sundaram, D. M., & Agilandeeswari, L. (2019) · IEEE Access · doi:10.1109/access.2019.2947717 · Cited by 45
Classification-Specific Parts for Improving Fine-Grained Visual Categorization
Korsch, D., Bodesheim, P., & Denzler, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33676-9_5 · Cited by 45
Deep Learning-Based Computer Vision for Surveillance in ITS: Evaluation of State-of-the-Art Methods
Xie, J., Zheng, Y., Du, R., Xiong, W., Cao, Y., Ma, Z., Cao, D., & Guo, J. H. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3065250 · Cited by 45
Guo, J. P., Shen, S., Xing, S., Chen, Y., Chen, F., Porter, E., Yu, H., & Huan, T. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c01309 · Cited by 45
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification
Wu, Y., Zhang, B., Yu, G., Zhang, W., Wang, B., Chen, T., & Fan, J. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475532 · Cited by 45
Attribute Prototype Network for Any-Shot Learning
Xu, W., Xian, Y., Wang, J., Schiele, B., & Akata, Z. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01613-9 · Cited by 47
Metadata
- Authors
- Zheng, H., Fu, J., Mei, T., & Luo, J.
- Affiliations
- University of Science and Technology of China (CN)
- Microsoft Research Asia (China) (CN)
- University of Rochester (US)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5219-5227
- OpenAlex
- W2773003563
- Cited by
- 1,045
- References
- 40
- Citations by year
- 2026: 33 · 2025: 71 · 2024: 126 · 2023: 153 · 2022: 143 · 2021: 206 · 2020: 145 · 2019: 111 · 2018: 56 · 2017: 1
- FWCI
- 25.02
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- fine-grained image classification, part localization, channel grouping, part classification, multi-task learning, representation learning, end-to-end learning, bird identification, aircraft recognition, vehicle identification
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
