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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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 · see citing works
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 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Jaderberg, M., Simonyan, K., Zisserman, A., & Kavukcuoglu, K. (2015) · arXiv · doi:10.48550/arxiv.1506.02025 · Cited by 5,137 · see citing works
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 4,749 · see citing works
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 3,523 · see citing works
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 2,207 · see citing works
Fu, J., Zheng, H., & Mei, T. (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.2017.476 · Cited by 1,459 · see citing works
Self-citationFine-Grained Visual Classification of AircraftPreprint
Maji, S., Rahtu, E., Kannala, J., Blaschko, M. B., & Vedaldi, A. (2013) · HAL · doi:10.48550/arxiv.1306.5151 · Cited by 1,250 · see citing works
Part-Based R-CNNs for Fine-Grained Category Detection
Zhang, N., Donahue, J., Girshick, R., & Darrell, T. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_54 · Cited by 1,212 · see citing works
A Visual Vocabulary for Flower Classification
Nilsback, M.-E., & Zisserman, A. (2006) · 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.2006.42 · Cited by 876 · see citing works
A large-scale car dataset for fine-grained categorization and verification
Yang, L., Luo, P., Loy, C. C., & Tang, X. (2015) · 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.2015.7299023 · Cited by 867 · see citing works
Xiao, T., Xu, Y., Yang, K., Zhang, J., Peng, Y., & Zhang, Z. (2015) · 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.2015.7298685 · Cited by 807 · see citing works
Learning Deep Representations of Fine-Grained Visual Descriptions
Reed, S., Akata, Z., Lee, H., & Schiele, B. (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.2016.13 · Cited by 802 · see citing works
Bird Species Categorization Using Pose Normalized Deep Convolutional NetsPreprint
Branson, S., Horn, G. V., Belongie, S., & Perona, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.2952 · Cited by 600 · see citing works
Part-Stacked CNN for Fine-Grained Visual Categorization
Huang, S., Xu, Z., Tao, D., & Zhang, 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.2016.132 · Cited by 482 · see citing works
Fine-grained recognition without part annotations
Krause, J., Jin, H., Yang, J., & Fei-Fei, L. (2015) · 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.2015.7299194 · Cited by 472 · see citing works
Diversified Visual Attention Networks for Fine-Grained Object Classification
Zhao, B., Wu, X., Feng, J., Peng, Q., & Yan, S. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2648498 · Cited by 424 · see citing works
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
Simon, M., & Rodner, E. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.136 · Cited by 400 · see citing works
Deep LAC: Deep localization, alignment and classification for fine-grained recognition
Lin, D., Shen, X., Lu, C., & Jia, J. (2015) · 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.2015.7298775 · Cited by 354 · see citing works
Picking Deep Filter Responses for Fine-Grained Image Recognition
Zhang, X., Xiong, H., Zhou, W., Lin, W., & Tian, Q. C. (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.2016.128 · Cited by 332 · see citing works
SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition
Zhang, H., Xu, T., Elhoseiny, M., Huang, X., Zhang, S., Elgammal, A., & Metaxas, 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.2016.129 · Cited by 301 · see citing works
Multiple Granularity Descriptors for Fine-Grained Categorization
Wang, D., Shen, Z., Shao, J., Zhang, W., Xue, X., & Zhang, Z. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.276 · Cited by 228 · see citing works
Learning Deep Representations of Fine-grained Visual DescriptionsPreprint
Reed, S., Akata, Z., Schiele, B., & Lee, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.05395 · Cited by 154 · see citing works
Boosted Convolutional Neural Networks
Moghimi, M. K., Belongie, S., Saberian, M., Yang, J. W., Vasconcelos, N. M., & Li, L.-J. (2016) · British Machine Vision Conference (BMVC) · doi:10.5244/c.30.24 · Cited by 136 · see citing works
Fisher vectors meet Neural Networks: A hybrid classification architecture
Perronnin, F., & Larlus, D. (2015) · 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.2015.7298998 · Cited by 134 · see citing works
Mining Discriminative Triplets of Patches for Fine-Grained Classification
Wang, Y., Choi, J., Morariu, V. I., & Davis, L. 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.2016.131 · Cited by 129 · see citing works
Revisiting the Fisher vector for fine-grained classification
Gosselin, P.-H., Murray, N., Jeǵou, H., & Perronnin, F. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.06.011 · Cited by 114 · see citing works
Parkhi, O., Vedaldi, A., Jawahar, C. V., & Zisserman, A. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126398 · Cited by 113 · see citing works
Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image RecognitionPreprint
Wei, X.-S., Xie, C.-W., & Wu, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06878 · Cited by 103 · see citing works
Liu, X., Xia, T., Wang, J., & Lin, Y. (2016) · arXiv (Cornell University) · Cited by 78 · see citing works
Relaxing from Vocabulary: Robust Weakly-Supervised Deep Learning for Vocabulary-Free Image Tagging
Fu, J., Wu, Y., Mei, T., Wang, J., Lu, H., & Rui, Y. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.230 · Cited by 38 · see citing works
Self-citationTagging Personal Photos with Transfer Deep Learning
Fu, J., Mei, T., Yang, K., Lu, H., & Rui, Y. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2736277.2741112 · Cited by 36 · see citing works
Self-citationImage Tag Refinement With View-Dependent Concept Representations
Fu, J., Wang, J., Rui, Y., Wang, X., Mei, T., & Lu, H. (2014) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2380211 · Cited by 30 · see citing works
Self-citation
Flagged first, then by citations
3 of the 40 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A survey of the recent architectures of deep convolutional neural networks
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137 · see citing works
Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566 · see citing works
“[8], [9], [15], [21], [31], [32], [34], [35] , [22], [26], [62], [63], [64], [65], [66], [67] , [41], [68], [69], [70], [71], [72], [73], [74] , [8], [34], [42], [43], [75], [76], [77], [78] , [27], [44], [45], [46], [79], [80], [81], [82] , [61], [83], [84], [85], [86], [87], [88], [89] , [47], [90], [91], [92], [93]…”
Multi-attentional Deepfake Detection
Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00222 · Cited by 1,019 · see citing works
“Fine-grained classification [50, 49, 13, 37, 12, 52, 47, 17, 10] is a challenging research task in computer vision, which captures the local discriminative features to distinguish different fine-grained categories.”
BackgroundOcclusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
Li, Y., Zeng, J., Shan, S., & Chen, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2886767 · Cited by 916 · see citing works
Dynamic 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 · see citing works
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chen, C., Li, O., Tao, C., Barnett, A. J., Su, J., & Rudin, C. (2018) · arXiv · doi:10.48550/arxiv.1806.10574 · Cited by 702 · see citing works
Learning to Navigate for Fine-Grained Classification
Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., & Wang, L. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_26 · Cited by 638 · see citing works
Destruction and Construction Learning for Fine-Grained Image Recognition
Chen, Y., Bai, Y., Zhang, W., & Mei, T. (2019) · 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.2019.00530 · Cited by 550 · see citing works
Self-citationLarge Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
Cui, Y., Song, Y., Sun, C., Howard, A., & Belongie, S. (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.2018.00432 · Cited by 522 · see citing works
Fine-Grained Image Analysis With Deep Learning: A Survey
Wei, X.-S., Song, Y.-Z., Aodha, O. M., Wu, J.-X., Peng, Y.-X., Tang, J.-H., Yang, J., & Belongie, S. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3126648 · Cited by 514 · see citing works
Zheng, H., Fu, J., Zha, Z.-J., & Luo, J. (2019) · 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.2019.00515 · Cited by 505 · see citing works
Self-citationHierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition
Yu, J., Tan, M., Zhang, H., Rui, Y., & Tao, D. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2932058 · Cited by 502 · see citing works
Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition
Wang, Y., Morariu, V. I., & Davis, L. S. (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.2018.00436 · Cited by 451 · see citing works
Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
Sun, M., Yuan, Y., Zhou, F., & Ding, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_49 · Cited by 441 · see citing works
Learning Attentive Pairwise Interaction for Fine-Grained Classification
Zhuang, P., Wang, Y., & Qiao, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7016 · Cited by 436 · see citing works
Fine-Grained Visual Classification via Progressive Multi-granularity Training of Jigsaw Patches
Du, R., Chang, D., Bhunia, A. K., Xie, J., Ma, Z., Song, Y.-Z., & Guo, J. H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58565-5_10 · Cited by 422 · see citing works
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
Rao, Y., Chen, G., Lu, J., & Zhou, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00106 · Cited by 404 · see citing works
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
Chang, D., Ding, Y., Xie, J., Bhunia, A. K., Li, X., Ma, Z., Wu, M., Guo, J. H., & Song, Y.-Z. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2973812 · Cited by 398 · see citing works
Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition
Yu, C., Zhao, X., Zheng, Q., Zhang, P., & You, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_35 · Cited by 364 · see citing works
A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN
Wang, H., Xu, J., Yan, R., & Gao, R. X. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2956332 · Cited by 362 · see citing works
Ge, W., Lin, X., & Yu, Y. (2019) · 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.2019.00315 · Cited by 330 · see citing works
Attention, please! A survey of neural attention models in deep learning
Correia, A. D. S., & Colombini, E. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10148-x · Cited by 330 · see citing works
Chen, Z., Xu, J., Peng, T., & Yang, C. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3059002 · Cited by 303 · see citing works
Viewpoint-Aware Attentive Multi-view Inference for Vehicle Re-identification
Yi, Z., & Shao, L. (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.2018.00679 · Cited by 293 · see citing works
Selective Sparse Sampling for Fine-Grained Image Recognition
Ding, Y., Zhou, Y., Zhu, Y., Ye, Q., & Jiao, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00670 · Cited by 293 · see citing works
Cross-X Learning for Fine-Grained Visual Categorization
Luo, W., Yang, X., Mo, X., Lu, Y., Davis, L. S., Li, J., Yang, J. J., & Lim, S.-N. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00833 · Cited by 292 · see citing works
Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification
Zhu, H., Ke, W., Li, D., Liu, J., Tian, L., & Shan, 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/cvpr52688.2022.00465 · Cited by 293 · see citing works
Neural Prototype Trees for Interpretable Fine-grained Image Recognition
Nauta, M., van Bree, R., & Seifert, C. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01469 · Cited by 269 · see citing works
DMA-Net: DeepLab With Multi-Scale Attention for Pavement Crack Segmentation
Sun, X., Xie, Y., Jiang, L., Cao, Y., & Liu, B. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3158670 · Cited by 269 · see citing works
Adaptive stock trading strategies with deep reinforcement learning methods
Wu, X., Chen, H., Wang, J., Troiano, L., Loia, V., & Fujita, H. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.066 · Cited by 266 · see citing works
Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention
Huynh, D., & Elhamifar, E. (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/cvpr42600.2020.00454 · Cited by 262 · see citing works
CrossTransformers: spatially-aware few-shot transferPreprint
Doersch, C., Gupta, A., & Zisserman, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.11498 · Cited by 255 · see citing works
Attribute Prototype Network for Zero-Shot LearningPreprint
Xu, W., Xian, Y., Wang, J., Schiele, B., & Akata, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.08290 · Cited by 254 · see citing works
Hu, T., Qi, H., Huang, Q., & Lu, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09891 · Cited by 252 · see citing works
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
Ji, R., Wen, L., Zhang, L., Du, D., Wu, Y., Zhao, C., Liu, X., & Huang, F. (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/cvpr42600.2020.01048 · Cited by 251 · see citing works
70 years of machine learning in geoscience in review
Dramsch, J. S. (2020) · Advances in geophysics · doi:10.1016/bs.agph.2020.08.002 · Cited by 234 · see citing works
Ding, Y., Ma, Z., Wen, S., Xie, J., Chang, D., Si, Z., Wu, M., & Ling, H. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3055617 · Cited by 228 · see citing works
70 years of machine learning in geoscience in review
Dramsch, J. S. (2020) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · Cited by 225 · see citing works
Less Is More: Picking Informative Frames for Video Captioning
Chen, Y., Wang, S., Zhang, W., & Huang, Q. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_22 · Cited by 222 · see citing works
HA-CCN: Hierarchical Attention-Based Crowd Counting Network
Sindagi, V. A., & Patel, V. M. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2928634 · Cited by 216 · see citing works
Learning a Mixture of Granularity-Specific Experts for Fine-Grained Categorization
Zhang, L., Huang, S., Liu, W., & Tao, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00842 · Cited by 214 · see citing works
Filtration and Distillation: Enhancing Region Attention for Fine-Grained Visual Categorization
Liu, C., Xie, H., Zha, Z.-J., Ma, L., Yu, L., & Zhang, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6822 · Cited by 204 · see citing works
Orthogonal Convolutional Neural Networks
Wang, J., Chen, Y., Chakraborty, R., & Yu, S. X. (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/cvpr42600.2020.01152 · Cited by 202 · see citing works
Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization
Liu, C., Weng, X., & Mu, Y. (2019) · 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.2019.00131 · Cited by 199 · see citing works
Cropping and attention based approach for masked face recognition
Li, Y., Guo, K., Lu, Y., & Liu, L. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02100-9 · Cited by 199 · see citing works
Channel Interaction Networks for Fine-Grained Image Categorization
Gao, Y., Han, X., Wang, X., Huang, W., & Scott, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6712 · Cited by 197 · see citing works
Multi-Scale Multi-View Deep Feature Aggregation for Food Recognition
Jiang, S., Min, W., Liu, L., & Luo, Z. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2929447 · Cited by 191 · see citing works
Remaining useful life prediction of bearings by a new reinforced memory GRU network
Zhou, J., Qin, Y., Chen, D., Liu, F., & Qian, Q. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101682 · Cited by 190 · see citing works
Goal-Oriented Gaze Estimation for Zero-Shot Learning
Liu, Y., Zhou, L., Bai, X. P., Huang, Y., Gu, L., Zhou, J. W., & Harada, T. (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/cvpr46437.2021.00379 · Cited by 184 · see citing works
Fine-Grained Visual Classification via Internal Ensemble Learning Transformer
Xu, Q., Wang, J., Jiang, B., & Luo, B. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3244340 · Cited by 184 · see citing works
Deep Learning on Small Datasets without Pre-Training using Cosine Loss
Barz, B., & Denzler, J. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093286 · Cited by 180 · see citing works
Zhang, X., & Chen, L. (2018) · International Conference on Learning Representations · Cited by 178 · see citing works
Action Recognition With Spatio–Temporal Visual Attention on Skeleton Image Sequences
Yang, Z., Li, Y., Yang, J., & Luo, J. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2864148 · Cited by 176 · see citing works
Self-citationKarnati, M., Seal, A., Bhattacharjee, D., Yazidi, A., & Krejcar, O. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3243661 · Cited by 176 · see citing works
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization
Zhang, F., Li, M., Zhai, G., & Liu, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67832-6_12 · Cited by 174 · see citing works
Patch-Gated CNN for Occlusion-aware Facial Expression Recognition
Li, Y., Zeng, J., Shan, S., & Chen, X. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545853 · Cited by 167 · see citing works
Lightweight transformer image feature extraction network
Zheng, W., Lu, S., Yang, Y., Yin, Z., & Yin, L. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1755 · Cited by 167 · see citing works
Interpretable and Accurate Fine-grained Recognition via Region Grouping
Huang, Z., & Li, Y. (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/cvpr42600.2020.00869 · Cited by 166 · see citing works
Barnett, A. J., Schwartz, F. R., Tao, C., Chen, C., Ren, Y., Lo, J. Y.-C., & Rudin, C. D. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00423-x · Cited by 162 · see citing works
Deep Residual Correction Network for Partial Domain Adaptation
Li, S., Liu, C. H., Lin, Q., Wen, Q., Su, L., Huang, G. M., & Ding, Z. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2964173 · Cited by 161 · see citing works
Learning to Zoom: A Saliency-Based Sampling Layer for Neural Networks
Recasens, A., Kellnhofer, P., Stent, S., Matusik, W., & Torralba, A. B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_4 · Cited by 157 · see citing works
Fine-Grained Recognition: Accounting for Subtle Differences between Similar Classes
Sun, G., Cholakkal, H., Khan, S. H., Khan, F. S., & Shao, L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6882 · Cited by 152 · see citing works
Chatterjee, J., & Dethlefs, N. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111051 · Cited by 148 · see citing works
Learning Deep Bilinear Transformation for Fine-grained Image RepresentationPreprint
Zheng, H., Fu, J., Zha, Z.-J., & Luo, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.03621 · Cited by 142 · see citing works
Self-citationFine-Grained Vehicle Classification With Channel Max Pooling Modified CNNs
Ma, Z., Chang, D., Xie, J., Ding, Y., Wen, S., Li, X., Si, Z., & Guo, J. H. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2899972 · Cited by 140 · see citing works
Device-Free Wireless Sensing for Human Detection: The Deep Learning Perspective
Zhang, R., Jing, X., Wu, S., Jiang, C., Mu, J., & Yu, F. R. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3024234 · Cited by 140 · see citing works
Your “Flamingo” is My “Bird”: Fine-Grained, or Not
Chang, D., Pang, K., Zheng, Y., Ma, Z., Song, Y.-Z., & Guo, J. H. (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/cvpr46437.2021.01131 · Cited by 138 · see citing works
AutoScale: Learning to Scale for Crowd Counting
Xu, C., Liang, D., Xu, Y., Bai, S., Zhan, W., Bai, X., & Tomizuka, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01542-z · Cited by 137 · see citing works
Lin, Z., Mu, S., Huang, F., Khattak, A. M., Wang, M., Gao, W., & Jia, J. (2019) · IEEE Access · doi:10.1109/access.2019.2891739 · Cited by 136 · see citing works
SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization
Sun, H., He, X., & Peng, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548308 · Cited by 136 · see citing works
Zheng, H., Fu, J., Zha, Z.-J., Luo, J., & Mei, T. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2921876 · Cited by 135 · see citing works
Self-citationP-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization
Han, J., Yao, X., Cheng, G., Feng, X., & Xu, D. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2933510 · Cited by 135 · see citing works
Du, R., Xie, J., Ma, Z., Chang, D., Song, Y.-Z., & Guo, J. H. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3126668 · Cited by 135 · see citing works
FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery
Singh, K. K., Ojha, U., & Lee, Y. J. (2019) · 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.2019.00665 · Cited by 132 · see citing works
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes
Donnelly, J. P., Barnett, A. J., & Chen, 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/cvpr52688.2022.01002 · Cited by 131 · see citing works
Local Semantic Siamese Networks for Fast Tracking
Liang, Z., & Shen, J. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2959256 · Cited by 130 · see citing works
Graph-based High-Order Relation Discovery for Fine-grained Recognition
Zhao, Y., Yan, K., Huang, F., & Li, J. (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/cvpr46437.2021.01483 · Cited by 124 · see citing works
Zhou, S., Wang, F., Huang, Z., & Wang, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00813 · Cited by 123 · see citing works
Multi-attention Meta Learning for Few-shot Fine-grained Image Recognition
Zhu, Y., Liu, C., & Jiang, S. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/152 · Cited by 120 · see citing works
Graph-Propagation Based Correlation Learning for Weakly Supervised Fine-Grained Image Classification
Wang, Z., Wang, S., Li, H., Dou, Z., & Li, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6912 · Cited by 119 · see citing works
Fine-Grained Object Classification via Self-Supervised Pose Alignment
Yang, X., Wang, Y., Chen, K., Xu, Y. D., & Tian, 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/cvpr52688.2022.00725 · Cited by 119 · see citing works
Interpretable Image Recognition by Constructing Transparent Embedding Space
Wang, J., Liu, H., Wang, X., & Jing, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00093 · Cited by 117 · see citing works
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
Huang, S., Wang, X., & Tao, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16255 · Cited by 115 · see citing works
Semantic-Guided Multi-Attention Localization for Zero-Shot LearningPreprint
Zhu, Y., Xie, J., Tang, Z., Peng, X., & Elgammal, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.00502 · Cited by 113 · see citing works
Zhang, Y., Tang, H., & Jia, K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_15 · Cited by 112 · see citing works
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
Chen, T., Wu, W., Gao, Y., Dong, L., Luo, X., & Lin, L. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240523 · Cited by 111 · see citing works
LSTM networks based on attention ordered neurons for gear remaining life prediction
Xiang, S., Qin, Y., Zhu, C., Wang, Y., & Chen, H. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.06.023 · Cited by 111 · see citing works
Generalized Zero-Shot Recognition Based on Visually Semantic Embedding
Zhu, P., Wang, H., & Saligrama, V. (2019) · 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.2019.00311 · Cited by 109 · see citing works
Chen, C., He, C., Hu, C., Pei, H., & Jiao, L. (2019) · IEEE Access · doi:10.1109/access.2019.2930939 · Cited by 108 · see citing works
Class attention network for image recognition
Cheng, G., Lai, P., Gao, D., & Han, J. (2023) · Science China Information Sciences · doi:10.1007/s11432-021-3493-7 · Cited by 106 · see citing works
Tang, Z., Tian, E., Wang, Y., Wang, L., & Yang, T. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2985159 · Cited by 105 · see citing works
Differentiable Patch Selection for Image Recognition
Cordonnier, J.-B., Mahendran, A., Dosovitskiy, A., Weissenborn, D., Uszkoreit, J., & Unterthiner, T. (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/cvpr46437.2021.00238 · Cited by 105 · see citing works
OAENet: Oriented attention ensemble for accurate facial expression recognition
Wang, Z., Zeng, F., Liu, S., & Zeng, B. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107694 · Cited by 104 · see citing works
Wang, Z., Wang, S., Yang, S., Li, H., Li, J., & Li, Z. (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/cvpr42600.2020.00977 · Cited by 102 · see citing works
Pay Attention to the Activations: A Modular Attention Mechanism for Fine-Grained Image Recognition
Rodríguez, P., Velazquez, D. A., Cucurull, G., Gonfaus, J. M., Roca, F. X., & Gonzàlez, J. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2928494 · Cited by 101 · see citing works
Interpretable Image Classification with Differentiable Prototypes Assignment
Rymarczyk, D., Struski, Ł., Górszczak, M., Lewandowska, K., Tabor, J., & Zieliński, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19775-8_21 · Cited by 100 · see citing works
SFRNet: Fine-Grained Oriented Object Recognition via Separate Feature Refinement
Cheng, G., Li, Q., Wang, G., Xie, X., Min, L., & Han, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3277626 · Cited by 100 · see citing works
Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification
Wei, X., Zhang, Y., Gong, Y., Zhang, J., & Zheng, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_22 · Cited by 99 · see citing works
Matching on Sets: Conquer Occluded Person Re-identification Without Alignment
Jia, M., Cheng, X., Zhai, Y., Lu, S., Ma, S., Tian, Y., & Zhang, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16260 · Cited by 99 · see citing works
Knowledge-Embedded Representation Learning for Fine-Grained Image Recognition
Chen, T., Lin, L., Chen, R., Yang, W., & Luo, X. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/87 · Cited by 98 · see citing works
- 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
