A survey on deep learning-based fine-grained object classification and semantic segmentation

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

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

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

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989

Proceedings of IEEE International Conference on Computer Vision

(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891

Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248

On the Properties of Neural Machine Translation: Encoder–Decoder Approaches

Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251

Selective Search for Object Recognition

Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980

Spatial transformer networks

Jaderberg, M., Simonyan, K., Zisserman, A., & Kavukcuoglu, K. (2015) · arXiv · doi:10.48550/arxiv.1506.02025 · Cited by 5,137

CNN Features Off-the-Shelf: An Astounding Baseline for Recognition

Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451

Learning Deconvolution Network for Semantic Segmentation

Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019

Recurrent Models of Visual AttentionPreprint

Mnih, V., Heess, N., Graves, A., & Kavukcuoglu, K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.6247 · Cited by 3,393

Recurrent Neural Network RegularizationPreprint

Zaremba, W., Sutskever, I., & Vinyals, O. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.2329 · Cited by 3,174

Long short-term memory recurrent neural network architectures for large scale acoustic modeling

Sak, H., Senior, A., & Beaufays, F. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-80 · Cited by 2,973

Mutual-information-based registration of medical images: a survey

Pluim, J. P. W., Maintz, J. B. A., & Viergever, M. A. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.815867 · Cited by 2,882

Edge Boxes: Locating Object Proposals from Edges

Zitnick, C. L., & Dollár, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_26 · Cited by 2,656

Learning Hierarchical Features for Scene Labeling

Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620

Describing Textures in the Wild

Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., & Vedaldi, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.461 · Cited by 2,438

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

A Novel Connectionist System for Unconstrained Handwriting Recognition

Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Horst Bunke, A., & Schmidhuber, J. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.137 · Cited by 2,041

Advances in Neural Information Processing Systems 5

Zemel, R. S., Williams, C. K. I., & Mozer, M. C. (1993) · Cited by 1,896

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_23 · Cited by 1,528

Going Deeper with Convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461

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 1,225

Simultaneous Detection and Segmentation

Hariharan, B., Arbeláez, P., Girshick, R., & Malik, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_20 · Cited by 1,189

Multiscale Combinatorial Grouping

Arbelaez, P., Pont-Tuset, J., Barron, J., Marques, F., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.49 · Cited by 1,155

BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

Dai, J., He, K., & Sun, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.191 · Cited by 1,124

On the Properties of Neural Machine Translation: Encoder-Decoder ApproachesPreprint

Cho, K., van Merriënboer, B., Bahdanau, D., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.1259 · Cited by 1,112

Multiple Object Recognition with Visual Attention

Ba, J., Mnih, V., & Kavukcuoglu, K. (2014) · arXiv · doi:10.48550/arxiv.1412.7755 · Cited by 808

The application of two-level attention models in deep convolutional neural network for fine-grained image classification

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

Action Recognition using Visual AttentionPreprint

Sharma, S., Kiros, R., & Salakhutdinov, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04119 · Cited by 623

CNN Features off-the-shelf: an Astounding Baseline for Recognition

Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · arXiv · doi:10.48550/arxiv.1403.6382 · Cited by 607

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

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

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 428

POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation

Berg, T., & Belhumeur, P. N. (2013) · 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.2013.128 · Cited by 380

Minimal gated unit for recurrent neural networks

Zhou, G., Wu, J., Zhang, C.-L., & Zhou, Z. (2016) · Machine Intelligence Research · doi:10.1007/s11633-016-1006-2 · Cited by 374

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

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 231

Dog Breed Classification Using Part Localization

Liu, J., Kanazawa, A., Jacobs, D. W., & Belhumeur, P. N. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_13 · Cited by 198

Fine-Grained Categorization by Alignments

Gavves, E., Fernando, B., Snoek, C. G. M., Smeulders, A. W. M., & Tuytelaars, T. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.215 · Cited by 179

BiCoS: A Bi-level co-segmentation method for image classification

Chai, Y., Lempitsky, V., & Zisserman, A. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126546 · Cited by 178

BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic SegmentationPreprint

Dai, J., He, K., & Sun, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.01640 · Cited by 146

Unsupervised Template Learning for Fine-Grained Object Recognition

Yang, S., Bo, L., Wang, J., & Shapiro, L. G. (2012) · Cited by 143

Image Classification and Retrieval are ONE

Xie, L., Hong, R., Zhang, B., & Tian, Q. (2015) · International Conference on Multimedia Retrieval · doi:10.1145/2671188.2749289 · Cited by 130

Untitled work

(1991) · Cited by 126

Attention for Fine-Grained CategorizationPreprint

Sermanet, P., Frome, A., & Real, E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.7054 · Cited by 119

Fully Convolutional Attention Localization Networks: Efficient Attention Localization for Fine-Grained RecognitionPreprint

Liu, X., Xia, T., Wang, J., & Lin, Y. (2016) · arXiv (Cornell University) · Cited by 78

Fine-grained classification via mixture of deep convolutional neural networks

Ge, Z., Bewley, A., McCool, C., Corke, P., Upcroft, B., & Sanderson, C. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477700 · Cited by 64

Learning fine-grained features via a CNN Tree for Large-scale Classification

Wang, Z., Wang, X., & Wang, G. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.09.061 · Cited by 58

Subset feature learning for fine-grained category classification

Ge, Z., McCool, C., Sanderson, C., & Corke, P. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301271 · Cited by 57

InterActive: Inter-Layer Activeness Propagation

Xie, L., Zheng, L., Wang, J., Yuille, A., & Tian, Q. (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.2016.36 · Cited by 48

Feature selection and feature learning for high-dimensional batch reinforcement learning: A survey

Liu, D., Li, H., & Wang, D. (2015) · Machine Intelligence Research · doi:10.1007/s11633-015-0893-y · Cited by 45

Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization

Shih, K. J., Mallya, A., Singh, S., & Hoiem, D. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.128 · Cited by 44

Efficient Large-Scale Structured Learning

Branson, S., Beijbom, O., & Belongie, S. (2013) · 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.2013.236 · Cited by 44

InterActive: Inter-Layer Activeness PropagationPreprint

Xie, L., Zheng, L., Wang, J., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.00052 · Cited by 9

Learning Fine-grained Features via a CNN Tree for Large-scale ClassificationPreprint

Wang, Z., Wang, X., & Wang, G. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04534 · Cited by 7

Fine-Grained Classification via Mixture of Deep Convolutional Neural NetworksPreprint

Ge, Z., Bewley, A., McCool, C., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.09209 · Cited by 4

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565

Attention gated networks: learning to leverage salient regions in medical images

Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014

A Survey of Deep Learning-Based Object Detection

Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019) · IEEE Access · doi:10.1109/access.2019.2939201 · Cited by 1,454

Convolutional neural network: a review of models, methodologies and applications to object detection

Dhillon, A., & Verma, G. K. (2020) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00203-0 · Cited by 1,119

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Zhao, H., Wang, R., Chong, Y., et al. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3065361 · Cited by 978

A Survey of Fine-Grained Image Classification Based on Deep Learning

Guo, C., Cui, H., Yu, K., & Xin-ping, M. O. (2019) · DEStech Transactions on Computer Science and Engineering · doi:10.12783/dtcse/ica2019/30636 · Cited by 1

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 Learning Based Fine Grained Image Classification

Vaidya, P., & Kamalapur, S. M. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i7s.7532

Fine-Grained Image Analysis With Deep Learning: A Survey

Wei, X.-S., Song, Y.-Z., Aodha, O. M., Wu, J., Peng, Y., Tang, J., Yang, J., & Belongie, S. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3126648 · Cited by 526

Fine-grained image classification using deep learning

Sun, D. (2021) · DR-NTU (Nanyang Technological University)