Caltech-UCSD Birds-200-2011 Dataset

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

The pascal visual object classes (VOC) challenge

Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107

Spatial transformer networks

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

CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features

Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., & Choe, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00612 · Cited by 5,400

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 without Forgetting

Li, Z., & Hoiem, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2773081 · Cited by 4,202

Continual lifelong learning with neural networks: A review

Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117

Class-Balanced Loss Based on Effective Number of Samples

Cui, Y., Jia, M., Lin, T.-Y., Song, Y., & Belongie, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00949 · Cited by 3,094

Generative Adversarial Text to Image Synthesis

Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv · doi:10.48550/arxiv.1605.05396 · Cited by 2,494

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., & He, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00143 · Cited by 1,923

Untitled work

(2016) · neural information processing systems · Cited by 1,572

Federated Learning for Healthcare Informatics

Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2019) · arXiv · doi:10.48550/arxiv.1911.06270 · Cited by 1,538

Federated Learning for Healthcare Informatics

Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00082-4 · Cited by 1,542

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

PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

Mallya, A., & Lazebnik, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00810 · Cited by 1,258

Fine-Grained Visual Classification of Aircraft

Maji, S., Rahtu, E., Kannala, J., Blaschko, M. B., & Vedaldi, A. (2013) · INRIA a CCSD electronic archive server · doi:10.48550/arxiv.1306.5151 · Cited by 1,084

Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning

Wang, X., Han, X., Huang, W., Dong, D., & Scott, M. R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00516 · Cited by 854

Interpretable Convolutional Neural Networks

Zhang, Q., Wu, Y. N., & Zhu, S.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00920 · Cited by 833

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776

Class-Incremental Learning: Survey and Performance Evaluation on Image Classification

Masana, M., Liu, X., Twardowski, B., Menta, M., Bagdanov, A. D., & van de Weijer, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3213473 · Cited by 740

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

Learning Without Forgetting

Li, Z., & Hoiem, D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_37 · Cited by 659

Recognition in Terra Incognita

Beery, S., Van Horn, G., & Perona, P. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_28 · Cited by 679

MirrorGAN: Learning Text-To-Image Generation by Redescription

Qiao, T., Zhang, J., Xu, D., & Tao, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00160 · Cited by 644

Zero-Shot Learning via Semantic Similarity Embedding

Zhang, Z., & Saligrama, V. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.474 · Cited by 629

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

Chao, W.-L., Changpinyo, S., Gong, B., & Sha, F. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_4 · Cited by 616

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

Measuring Catastrophic Forgetting in Neural Networks

Kemker, R., McClure, M., Abitino, A., Hayes, T., & Kanan, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11651 · Cited by 672

Knockoff Nets: Stealing Functionality of Black-Box Models

Orekondy, T., Schiele, B., & Fritz, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00509 · Cited by 549

Low-Shot Learning with Imprinted Weights

Qi, H., Brown, M., & Lowe, D. G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00610 · Cited by 539

Generating Visual Explanations

Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534

A Survey of Zero-Shot Learning: Settings, Methods, and Applications

Wang, W., Zheng, V. W., Yu, H., & Miao, C. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3293318 · Cited by 528

TransFG: A Transformer Architecture for Fine-Grained Recognition

He, J., Chen, J.-N., Liu, S., Kortylewski, A., Yang, C., Bai, Y., & Wang, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19967 · Cited by 551

Transductive Multi-View Zero-Shot Learning

Fu, Y., Hospedales, T. M., Xiang, T., & Gong, S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2408354 · Cited by 514

Few-Shot Class-Incremental Learning

Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., & Gong, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01220 · Cited by 499

SpotTune: Transfer Learning Through Adaptive Fine-Tuning

Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T., & Feris, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00494 · Cited by 506

Fully Automated Deep Learning System for Bone Age Assessment

Lee, H., Tajmir, S., Lee, J., Zissen, M., Yeshiwas, B. A., Alkasab, T. K., Choy, G., & Do, S. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9955-8 · Cited by 452

Learn To Pay Attention

Jetley, S., Lord, N. A., Lee, N., & Torr, P. H. S. (2018) · arXiv · doi:10.48550/arxiv.1804.02391 · Cited by 383

Few-Shot Incremental Learning with Continually Evolved Classifiers

Zhang, C., Song, N., Lin, G., Zheng, Y., Pan, P., & Xu, Y. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01227 · Cited by 382

Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

Rao, Y., Chen, G., Lu, J., & Zhou, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00106 · Cited by 380

Class-Incremental Learning: A Survey

Zhou, D.-W., Wang, Q.-W., Qi, Z.-H., Ye, H.-J., Zhan, D.-C., & Liu, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3429383 · Cited by 376

TediGAN: Text-Guided Diverse Face Image Generation and Manipulation

Xia, W., Yang, Y., Xue, J.-H., & Wu, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00229 · Cited by 376

Low-Rank Bilinear Pooling for Fine-Grained Classification

Kong, S., & Fowlkes, C. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.743 · Cited by 367

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Tack, J., Mo, S., Jeong, J., & Shin, J. (2020) · arXiv · doi:10.48550/arxiv.2007.08176 · Cited by 351

Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition

Lin, T.-Y., RoyChowdhury, A., & Maji, S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723400 · Cited by 328

Google Landmarks Dataset v2 – A Large-Scale Benchmark for Instance-Level Recognition and Retrieval

Weyand, T., Araujo, A., Cao, B., & Sim, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00265 · Cited by 303

A Survey of Privacy Attacks in Machine Learning

Rigaki, M., & Garcia, S. (2024) · ACM Computing Surveys · doi:10.1145/3624010 · Cited by 285

Weakly Supervised Object Localization and Detection: A Survey

Zhang, D., Han, J., Cheng, G., & Yang, M.-H. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3074313 · Cited by 277

Semantic Image Synthesis via Adversarial Learning

Dong, H., Yu, S., Wu, C., & Guo, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.608 · Cited by 276

A survey on few-shot class-incremental learning

Tian, S., Li, L., Li, W., Ran, H., Ning, X., & Tiwari, P. (2024) · Neural Networks · doi:10.1016/j.neunet.2023.10.039 · Cited by 220

Untitled work

(2019) · arXiv (Cornell University) · Cited by 214

Lifelong Learning via Progressive Distillation and Retrospection

Hou, S., Pan, X., Loy, C. C., Wang, Z., & Lin, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_27 · Cited by 190

Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning

Cheraghian, A., Rahman, S., Fang, P., Roy, S. K., Petersson, L., & Harandi, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00256 · Cited by 201

A Benchmark for Automatic Visual Classification of Clinical Skin Disease Images

Sun, X., Yang, J., Sun, M., & Wang, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_13 · Cited by 197

Dataset distillation

Wang, T., Zhu, J.-Y., Torralba, A., & Efros, A. A. (2018) · arXiv · doi:10.48550/arxiv.1811.10959 · Cited by 194

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models

Shi, Y., Siddharth, N., Paige, B., & Torr, P. H. S. (2019) · arXiv · doi:10.48550/arxiv.1911.03393 · Cited by 186

Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning

Hao, F., He, F., Cheng, J., Wang, L., Cao, J., & Tao, D. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00855 · Cited by 167

Few-Shot Class-Incremental Learning via Relation Knowledge Distillation

Dong, S., Hong, X., Tao, X., Chang, X., Wei, X., & Gong, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16213 · Cited by 161

Growing a Brain: Fine-Tuning by Increasing Model Capacity

Wang, Y.-X., Ramanan, D., & Hebert, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.323 · Cited by 154

Fine-Grained Recognition in the Wild: A Multi-task Domain Adaptation Approach

Gebru, T., Hoffman, J., & Fei-Fei, L. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.151 · Cited by 152

Untitled work

(2020) · doi:10.1007/978-3-030-58586-0_27 · Cited by 156

Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut

Wang, Y., Shen, X., Hu, S. X., Yuan, Y., Crowley, J. L., & Vaufreydaz, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01414 · Cited by 155

Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning

Baik, S., Choi, J., Kim, H., Cho, D., Min, J., & Lee, K. M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00933 · Cited by 163

Incentivizing High Quality Crowdwork

Ho, C.-J., Slivkins, A., Suri, S., & Vaughan, J. W. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741102 · Cited by 146

Presence-Only Geographical Priors for Fine-Grained Image Classification

Aodha, O. M., Cole, E., & Perona, P. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00969 · Cited by 149

Training Vision Transformers for Image Retrieval

El-Nouby, A., Neverova, N., Laptev, I., & Jégou, H. (2021) · arXiv · doi:10.48550/arxiv.2102.05644 · Cited by 127

Mixed-Privacy Forgetting in Deep Networks

Golatkar, A., Achille, A., Ravichandran, A., Polito, M., & Soatto, S. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00085 · Cited by 125

Neural Architecture Search for Transformers: A Survey

Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3212767 · Cited by 118

MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning

Zhao, H., Fu, Y., Kang, M., Tian, Q., Wu, F., & Li, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3133897 · Cited by 115

Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval

Guo, Y., Ding, G., Liu, L., Han, J., & Shao, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2652730 · Cited by 108

Few-Shot Class-Incremental Learning via Class-Aware Bilateral Distillation

Zhao, L., Lu, J., Xu, Y., Cheng, Z., Guo, D., Niu, Y., & Fang, X. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01139 · Cited by 109

Debugging Tests for Model Explanations

Adebayo, J., Muelly, M., Liccardi, I., & Kim, B. (2020) · arXiv · doi:10.48550/arxiv.2011.05429 · Cited by 88

Self-supervised Learning is More Robust to Dataset Imbalance

Liu, H., HaoChen, J. Z., Gaidon, A., & Ma, T. (2021) · arXiv · doi:10.48550/arxiv.2110.05025 · Cited by 84

Classification-Driven Dynamic Image Enhancement

Sharma, V., Diba, A., Neven, D., Brown, M. S., Gool, L. V., & Stiefelhagen, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00424 · Cited by 66

Generating Visual Explanations

Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · arXiv · doi:10.48550/arxiv.1603.08507 · Cited by 64

Learning with Selective Forgetting

Shibata, T., Irie, G., Ikami, D., & Mitsuzumi, Y. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/137 · Cited by 47

Few-Shot Class Incremental Learning Leveraging Self-Supervised Features

Ahmad, T., Dhamija, A. R., Cruz, S., Rabinowitz, R., Li, C., Jafarzadeh, M., & Boult, T. E. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00435 · Cited by 41

Multimodal Parameter-Efficient Few-Shot Class Incremental Learning

D’Alessandro, M., Alonso, A., Calabrés, E., & Galar, M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw60793.2023.00364 · Cited by 40

LAFITE: Towards Language-Free Training for Text-to-Image Generation

Zhou, Y., Zhang, R., Chen, C., Li, C., Tensmeyer, C., Yu, T., Gu, J., Xu, J., & Sun, T. (2021) · arXiv · doi:10.48550/arxiv.2111.13792 · Cited by 38

ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition

Massiceti, D., Zintgraf, L., Bronskill, J., Theodorou, L., Harris, M. T., Cutrell, E., Morrison, C., Hofmann, K., & Stumpf, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01064 · Cited by 33

A Deep Multi-Modal Explanation Model for Zero-Shot Learning

Liu, Y., & Tuytelaars, T. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2975980 · Cited by 31

Birds of a Feather: Capturing Avian Shape Models from Images

Wang, Y., Kolotouros, N., Daniilidis, K., & Badger, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01450 · Cited by 28

SSFE-Net: Self-Supervised Feature Enhancement for Ultra-Fine-Grained Few-Shot Class Incremental Learning

Pan, Z., Yu, X., Zhang, M., & Gao, Y. (2023) · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00621 · Cited by 28

Research progress of zero-shot learning beyond computer vision

Cao, W., Zhou, C., Wu, Y., Ming, Z., Xu, Z., & Zhang, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60239-0_36 · Cited by 21

GAN-MVAE: a discriminative latent feature generation framework for generalized zero-shot learning

Ma, P., Lu, H., Yang, B., & Ran, W. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.02.002 · Cited by 18

Focus your attention: A focal attention for multimodal learning

Liu, C., Mao, Z., Zhang, T., Liu, A.-A., Wang, B., & Zhang, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3046855 · Cited by 17

Untitled work

(2019) · arXiv (Cornell University) · Cited by 17

Multimodal Federated Learning: A Survey

Che, L., Wang, J., Zhou, Y., & Ma, F. (2023) · doi:10.20944/preprints202307.1420.v1 · Cited by 127

TICS: text–image-based semantic CAPTCHA synthesis via multi-condition adversarial learning

Jia, X., Xiao, J., & Wu, C. (2022) · The Visual Computer · doi:10.1007/s00371-021-02061-1 · Cited by 13

An Interactive Scene Generation Using Natural Language

Cheng, Y., Shi, Y., Sun, Z., Feng, D., & Dong, L. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8794327 · Cited by 5