Enhanced Feature Representations for Low-Resolution Fine-Grained Image Recognition via Categorical Knowledge Guidance (original) (raw)
References
Huang C, Wu Z, Wen J, Xu Y, Jiang Q, Wang Y. Abnormal event detection using deep contrastive learning for intelligent video surveillance system. IEEE Trans. Industrial informatics, 2022, 18(8): 5171–5179. DOI: https://doi.org/10.1109/TII.2021.3122801. Article Google Scholar
Huang C, Wen J, Xu Y, Jiang Q, Yang J, Wang Y, Zhang D. Self-supervised attentive generative adversarial networks for video anomaly detection. IEEE Trans. Neural Networks and Learning Systems, 2023, 34(11): 9389–9403. DOI: https://doi.org/10.1109/TNNLS.2022.3159538. Article Google Scholar
Huang C, Yang Z, Wen J, Xu Y, Jiang Q, Yang J, Wang Y. Self-supervision-augmented deep autoencoder for unsupervised visual anomaly detection. IEEE Trans. Cybernetics, 2022, 52(12): 13834–13847. DOI: https://doi.org/10.1109/TCYB.2021.3127716. Article Google Scholar
Wang P, Yang H, Han G, Yu R, Yang L, Sun G, Qi H, Wei X, Zhang Q. Decentralized navigation with heterogeneous federated reinforcement learning for UAV-enabled mobile edge computing. IEEE Trans. Mobile Computing, 2024, 23(12): 13621–13638. DOI: https://doi.org/10.1109/TMC.2024.3439696. Article Google Scholar
Huang C, Liu C, Wen J, Wu L, Xu Y, Jiang Q, Wang Y. Weakly supervised video anomaly detection via self-guided temporal discriminative transformer. IEEE Trans. Cybernetics, 2024, 54(5): 3197–3210. DOI: https://doi.org/10.1109/TCYB.2022.3227044. Article Google Scholar
Singh M, Nagpal S, Singh R, Vatsa M. Dual directed capsule network for very low resolution image recognition. In Proc. the 2019 IEEE/CVF International Conference on Computer Vision, Oct. 27-Nov. 2, 2019, pp.340–349. DOI: https://doi.org/10.1109/ICCV.2019.00043. Google Scholar
Chen Y C, Li Y, Du X, Wang Y C F. Learning resolution-invariant deep representations for person re-identification. In Proc. the 33rd AAAI Conference on Artificial Intelligence, Jan. 27 -Feb. 1, 2019, pp.8215–8222. DOI: https://doi.org/10.1609/aaai.v33i01.33018215. Google Scholar
Li Y J, Chen Y C, Lin Y Y, Du X, Wang Y C F. Recover and identify: A generative dual model for cross-resolution person re-identification. In Proc. the 2019 IEEE/CVF International Conference on Computer Vision, Oct. 27-Nov. 2, 2019, pp.8089–8098. DOI: https://doi.org/10.1109/ICCV.2019.00818. Google Scholar
Jiao J, Zheng W S, Wu A, Zhu X, Gong S. Deep low-resolution person re-identification. In Proc. the 32nd AAAI Conference on Artificial Intelligence, Feb. 2018, pp.6967–6974. DOI: https://doi.org/10.1609/AAAI.V32I1.12284. Google Scholar
Zhang G, Ge Y, Dong Z, Wang H, Zheng Y, Chen S. Deep high-resolution representation learning for cross-resolution person re-identification. IEEE Trans. Image Processing, 2021, 30: 8913–8925. DOI: https://doi.org/10.1109/TIP.2021.3120054. Article Google Scholar
Wang Z, Ye M, Yang F, Bai X, Satoh S. Cascaded SRGAN for scale-adaptive low resolution person re-identification. In Proc. the 27th International Joint Conference on Artificial Intelligence, Jul. 2018, pp.3891–3897. DOI: https://doi.org/10.24963/ijcai.2018/541. Google Scholar
Han K, Huang Y, Chen Z, Wang L, Tan T. Prediction and recovery for adaptive low-resolution person re-identification. In Proc. the 16th European Conference, Aug. 2020, pp.193–209. DOI: https://doi.org/10.1007/978-3-030-58574-7_12. Google Scholar
Zhang G, Chen Y, Lin W, Chandran A K, Jing X. Low resolution information also matters: Learning multi-resolution representations for person re-identification. In Proc. the 13th International Joint Conference on Artificial Intelligence, Aug. 2021. DOI: https://doi.org/10.24963/IJCAI.2021/179. Google Scholar
Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S. End-to-end object detection with transformers. In Proc. the 16th European Conference, Aug. 2020, pp.213–229. DOI: https://doi.org/10.1007/978-3-030-58452-8_13. Google Scholar
Liu C, Xie H, Zha Z J, Ma L, Yu L, Zhang Y. Filtration and distillation: Enhancing region attention for finegrained visual categorization. In Proc. the 34th AAAI Conference on Artificial Intelligence, Feb. 2020, pp.11555–11562. DOI: https://doi.org/10.1609/AAAI.V34I07.6822. Google Scholar
Ge W, Lin X, Yu Y. Weakly supervised complementary parts models for fine-grained image classification from the bottom up. In Proc. the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2019, pp.3034–3043. DOI: https://doi.org/10.1109/CVPR.2019.00315. Google Scholar
Yang X, Wang Y, Chen K, Xu Y, Tian Y. Fine-grained object classification via self-supervised pose alignment. In Proc. the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2022, pp.7389–7398. DOI: https://doi.org/10.1109/CVPR52688.2022.00725. Google Scholar
Ren S, He K, Girshick R B, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. DOI: https://doi.org/10.1109/TPAMI.2016.2577031. Article Google Scholar
Zheng H, Fu J, Mei T, Luo J. Learning multi-attention convolutional neural network for fine-grained image recognition. In Proc. the 2017 IEEE International Conference on Computer Vision, Oct. 2017, pp.5219–5227. DOI: https://doi.org/10.1109/ICCV.2017.557. Google Scholar
Zheng H, Fu J, Zha Z J, Luo J. Looking for the devil in the details: Learning trilinear attention sampling network for fine-grained image recognition. In Proc. the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2019, pp.5007–5016. DOI: https://doi.org/10.1109/CVPR.2019.00515. Google Scholar
Ding Y, Zhou Y, Zhu Y, Ye Q, Jiao J. Selective sparse sampling for fine-grained image recognition. In Proc. the 2019 IEEE/CVF International Conference on Computer Vision, Oct. 27-Nov. 2, 2019, pp.6598–6607. DOI: https://doi.org/10.1109/ICCV.2019.00670. Google Scholar
Ji R, Wen L, Zhang L, Du D, Wu Y, Zhao C, Liu X, Huang F. Attention convolutional binary neural tree for fine-grained visual categorization. In Proc. the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2020, pp.10465–10474. DOI: https://doi.org/10.1109/CVPR42600.2020.01048. Google Scholar
Ding Y, Han Z, Zhou Y, Zhu Y, Chen J, Ye Q, Jiao J. Dynamic perception framework for fine-grained recognition. IEEE Trans. Circuits and Systems for Video Technology, 2022, 32(3): 1353–1365. DOI: https://doi.org/10.1109/TCSVT.2021.3069835. Article Google Scholar
Rao Y, Chen G, Lu J, Zhou J. Counterfactual attention learning for fine-grained visual categorization and re-identification. In Proc. the 2021 IEEE/CVF International Conference on Computer Vision, Oct. 2021, pp.1005–1014. DOI: https://doi.org/10.1109/ICCV48922.2021.00106. Google Scholar
He J, Chen J N, Liu S, Kortylewski A, Yang C, Bai Y, Wang C. TransFG: A transformer architecture for finegrained recognition. In Proc. the 36th AAAI Conference on Artificial Intelligence, Feb. 22-Mar. 1, 2022, pp.852–860. DOI: https://doi.org/10.1609/AAAI.V36I1.19967. Google Scholar
Hu Y, Jin X, Zhang Y, Hong H, Zhang J, He Y, Xue H. RAMS-trans: Recurrent attention multi-scale transformer for fine-grained image recognition. In Proc. the 29th ACM International Conference on Multimedia, Oct. 2021, pp.4239–4248. DOI: https://doi.org/10.1145/3474085.3475561. Chapter Google Scholar
Sun H, He X, Peng Y. SIM-trans: Structure information modeling transformer for fine-grained visual categorization. In Proc. the 30th ACM International Conference on Multimedia, Oct. 2022, pp.5853–5861. DOI: https://doi.org/10.1145/3503161.3548308. Chapter Google Scholar
Mao S, Zhang S, Yang M. Resolution-invariant person reidentification. In Proc. the 28th International Joint Conference on Artificial Intelligence, Aug. 2019, pp.883–889. DOI: https://doi.org/10.24963/ijcai.2019/124. Google Scholar
Yan T, Shi J, Li H, Luo Z, Wang Z. Discriminative information restoration and extraction for weakly supervised low-resolution fine-grained image recognition. Pattern Recognition, 2022, 127: 108629. DOI: https://doi.org/10.1016/j.patcog.2022.108629. Article Google Scholar
Yan T, Li H, Sun B, Wang Z, Luo Z. Discriminative feature mining and enhancement network for low-resolution fine-grained image recognition. IEEE Trans. Circuits and Systems for Video Technology, 2022, 32(8): 5319–5330. DOI: https://doi.org/10.1109/TCSVT.2022.3144186. Article Google Scholar
Haris M, Shakhnarovich G, Ukita N. Deep back-projection networks for super-resolution. In Proc. the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2018, pp.1664–1673. DOI: https://doi.org/10.1109/CVPR.2018.00179. Chapter Google Scholar
Wu Z, Xiong Y, Yu S X, Lin D. Unsupervised feature learning via non-parametric instance discrimination. In Proc. the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2018, pp.3733–3742. DOI: https://doi.org/10.1109/CVPR.2018.00393. Chapter Google Scholar
Chen W Y, Liu Y C, Kira Z, Wang Y C F, Huang J B. A closer look at few-shot classification. In Proc. the 7th International Conference on Learning Representations, May 2019. Google Scholar
Gidaris S, Komodakis N. Dynamic few-shot visual learning without forgetting. In Proc. the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2018, pp.4367–4375. DOI: https://doi.org/10.1109/CVPR.2018.00459. Chapter Google Scholar
Wang X, Zhu L, Wang H, Yang Y. Interactive prototype learning for egocentric action recognition. In Proc. the 2021 IEEE/CVF International Conference on Computer Vision, Oct. 2021, pp.8148–8157. DOI: https://doi.org/10.1109/ICCV48922.2021.00806. Google Scholar
Wah C, Branson S, Welinder P, Perona P, Belongie S. The caltech-UCSD birds-200-2011 dataset. Technical Report, CNS-TR-2011-001, California Institute of Technology, 2011/, May 2025. Google Scholar
Krause J, Stark M, Deng J, Li F F. 3D object representations for fine-grained categorization. In Proc. the 2013 IEEE International Conference on Computer Vision Workshops, Dec. 2013, pp.554–561. DOI: https://doi.org/10.1109/ICCVW.2013.77. Google Scholar
Maji S, Rahtu E, Kannala J, Blaschko M, Vedaldi A. Fine-grained visual classification of aircraft. arXiv: 1306.5151, 2013. https://arxiv.org/abs/1306.5151, Mar. 2025.
Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W. Photo-realistic single image super-resolution using a generative adversarial network. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Jul. 2017, pp.105–114. DOI: https://doi.org/10.1109/CVPR.2017.19. Google Scholar
Tong T, Li G, Liu X, Gao Q. Image super-resolution using dense skip connections. In Proc. the 2017 IEEE International Conference on Computer Vision, Oct. 2017, pp.4809–4817. DOI: https://doi.org/10.1109/ICCV.2017.514. Google Scholar
Zhu X, Li Z, Zhang X, Li H, Xue Z, Wang L. Generative adversarial image super-resolution through deep dense skip connections. Computer Graphics Forum, 2018, 37(7): 289–300. DOI: https://doi.org/10.1111/cgf.13568. Article Google Scholar
Peng J, Xiao C, Li Y. RP2K: A large-scale retail product dataset for fine-grained image classification. arXiv: 2006.12634, 2020. https://arxiv.org/abs/2006.12634, Mar. 2025.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In Proc. the 3rd International Conference on Learning Representations, May 2015. Google Scholar
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2016, pp.770–778. DOI: https://doi.org/10.1109/CVPR.2016.90. Google Scholar
Deng J, Dong W, Socher R, Li L J, Li K, Li F F. ImageNet: A large-scale hierarchical image database. In Proc. the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2009, pp.248–255. DOI: https://doi.org/10.1109/CVPR.2009.5206848. Chapter Google Scholar
Chang D, Ding Y, Xie J, Bhunia A K, Li X, Ma Z, Wu M, Guo J, Song Y. The devil is in the channels: Mutual-channel loss for fine-grained image classification. IEEE Trans. Image Processing, 2020, 29: 4683–4695. DOI: https://doi.org/10.1109/TIP.2020.2973812. Article Google Scholar
Wang Y, Morariu V I, Davis L S. Learning a discriminative filter bank within a CNN for fine-grained recognition. In Proc. the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2018, pp.4148–4157. DOI: https://doi.org/10.1109/CVPR.2018.00436. Chapter Google Scholar
Du R, Chang D, Bhunia A K, Xie J, Ma Z, Song Y Z, Guo J. Fine-grained visual classification via progressive multi-granularity training of jigsaw patches. In Proc. the 16th European Conference, Aug. 2020, pp.153–168. DOI: https://doi.org/10.1007/978-3-030-58565-5_10. Google Scholar