Fetching the paper…
Reading the bibliography…
Advances in deep learning are re-defining how visual data is processed and understand by the machines.
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a” siamese” time delay neural network. Advances in neural information processing systems 6
1993
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86
1998
Earlier work this paper cites.
Thompson, B.: Canonical correlation analysis. (2000)
2000
Earlier work this paper cites.
Simard, P.Y., Steinkraus, D., Platt, J.C., et al
2003
Earlier work this paper cites.
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., Huang, F., et al.: A tutorial on energy-based learning. Predicting structured data 1
2006
Earlier work this paper cites.
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. Advances in neural information processing systems 19
2006
Earlier work this paper cites.
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1735–1742 (2006). IEEE
2006
Earlier work this paper cites.
Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.Y.: Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning, pp. 759–766 (2007)
2007
Earlier work this paper cites.
Nilsback, M.-E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pp. 722–729 (2008). IEEE
2008
Earlier work this paper cites.
Salakhutdinov, R., Hinton, G.: Deep boltzmann machines. In: Artificial Intelligence and Statistics, pp. 448–455 (2009). PMLR
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22
2009
Earlier work this paper cites.
Gallier, J.: Geometric Methods and Applications: for Computer Science and Engineering vol. 38. Springer, ??? (2011)
2011
Earlier work this paper cites.
Struik, D.J.: Lectures on Analytic and Projective Geometry. Courier Corporation, ??? (2011)
2011
Earlier work this paper cites.
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3d object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 554–561 (2013)
2013
Earlier work this paper cites.
Kingma, D.P., Mohamed, S., Jimenez Rezende, D., Welling, M.: Semi-supervised learning with deep generative models. Advances in neural information processing systems 27
2014
Earlier work this paper cites.
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? Advances in neural information processing systems 27
2014
Earlier work this paper cites.
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717–1724 (2014)
2014
Earlier work this paper cites.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pp. 740–755 (2014). Springer
2014
Earlier work this paper cites.
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13, pp. 818–833 (2014). Springer
2014
Earlier work this paper cites.
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422–1430 (2015)
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115
2015
Earlier work this paper cites.
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234–241 (2015). Springer
2015
Earlier work this paper cites.
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. Advances in neural information processing systems 28
2015
Earlier work this paper cites.
Karargyris, A.: Color space transformation network. arXiv preprint arXiv:1511.01064 (2015)
2015
Earlier work this paper cites.
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: Feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)
2016
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14, pp. 649–666 (2016). Springer
2016
Earlier work this paper cites.
Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: European Conference on Computer Vision, pp. 69–84 (2016). Springer
2016
Earlier work this paper cites.
Alexey, D., Fischer, P., Tobias, J., Springenberg, M.R., Brox, T.: Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE TPAMI 38
2016
Earlier work this paper cites.
Misra, I., Zitnick, C.L., Hebert, M.: Shuffle and learn: unsupervised learning using temporal order verification. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 527–544 (2016). Springer
2016
Earlier work this paper cites.
Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571 (2016). Ieee
2016
Earlier work this paper cites.
Tarasiuk, P., Pryczek, M.: Geometric transformations embedded into convolutional neural networks. Journal of Applied Computer Science, Vol. 24, No. 3, Wydawnictwo Politechniki Łódzkiej, Łódź 2016, ISSN 1507-0360. (2016)
2016
Earlier work this paper cites.
Gal, Y., Islam, R., Ghahramani, Z.: Deep bayesian active learning with image data. In: International Conference on Machine Learning, pp. 1183–1192 (2017). PMLR
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 843–852 (2017)
2017
Earlier work this paper cites.
Villegas, R., Yang, J., Hong, S., Lin, X., Lee, H.: Decomposing motion and content for natural video sequence prediction. In: International Conference on Learning Representations (ICLR) (2017)
2017
Earlier work this paper cites.
Arandjelović, R., Zisserman, A.: Look, listen and learn. In: Proc. ICCV, vol. 3, p. 9 (2017)
2017
Earlier work this paper cites.
Doersch, C., Zisserman, A.: Multi-task self-supervised visual learning. In: IEEE International Conference on Computer Vision (ICCV) (2017)
2017
Earlier work this paper cites.
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 633–641 (2017)
2017
Earlier work this paper cites.
Vu, H.T., Huang, C.-C.: A multi-task convolutional neural network with spatial transform for parking space detection. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 1762–1766 (2017). IEEE
2017
Earlier work this paper cites.
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
2017
Earlier work this paper cites.
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
2017
Earlier work this paper cites.
Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. In: International Conference on Machine Learning, pp. 1885–1894 (2017). PMLR
2017
Earlier work this paper cites.
Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: International Conference on Learning Representations (2018)
2018
Earlier work this paper cites.
Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 132–149 (2018)
2018
Earlier work this paper cites.
Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733–3742 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Singh, D.: Self-supervised learning gets us closer to autonomous learning. Aug (2018)
2018
Earlier work this paper cites.
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 418–434 (2018)
2018
Earlier work this paper cites.
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., Belongie, S.: The inaturalist species classification and detection dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8769–8778 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51
2018
Earlier work this paper cites.
Kingma, D.P., Welling, M., et al
2019
Earlier work this paper cites.
Asano, Y., Rupprecht, C., Vedaldi, A.: Self-labelling via simultaneous clustering and representation learning. In: International Conference on Learning Representations (2019)
2019
Earlier work this paper cites.
Zhuang, C., Zhai, A.L., Yamins, D.: Local aggregation for unsupervised learning of visual embeddings. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6002–6012 (2019)
2019
Cited alongside, same era.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al
2019
Cited alongside, same era.
Cai, Z., Vasconcelos, N.: Cascade r-cnn: High quality object detection and instance segmentation. IEEE transactions on pattern analysis and machine intelligence 43
2019
Cited alongside, same era.
Barbu, A., Mayo, D., Alverio, J., Luo, W., Wang, C., Gutfreund, D., Tenenbaum, J., Katz, B.: Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. Advances in neural information processing systems 32
2019
Cited alongside, same era.
Wang, K., Fang, B., Qian, J., Yang, S., Zhou, X., Zhou, J.: Perspective transformation data augmentation for object detection. IEEE Access 8
Hassan, M., Ali, S., Sanaullah, M., Shahzad, K., Mushtaq, S., Abbasi, R., Ali, Z., Alquhayz, H.: Drug response prediction of liver cancer cell line using deep learning. Computers, Materials & Continua (CMC) 70
2022
Later among the works it cites.
Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., Han, X.: Transformer-based unsupervised contrastive learning for histopathological image classification. Medical image analysis 81
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
Touvron, H., Vedaldi, A., Douze, M., Jégou, H.: Fixing the train-test resolution discrepancy. Advances in neural information processing systems 32
2019
Cited alongside, same era.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al
2020
Cited alongside, same era.
2020
Cited alongside, same era.
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738 (2020)
2020
Cited alongside, same era.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607 (2020). PMLR
2020
Cited alongside, same era.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Communications of the ACM 63
2020
Cited alongside, same era.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research 21
2020
Cited alongside, same era.
2022
Later among the works it cites.
Baevski, A., Hsu, W.-N., Xu, Q., Babu, A., Gu, J., Auli, M.: Data2vec: A general framework for self-supervised learning in speech, vision and language. In: International Conference on Machine Learning, pp. 1298–1312 (2022). PMLR
2022
Later among the works it cites.
Dong, X., Bao, J., Zhang, T., Chen, D., Zhang, W., Yuan, L., Chen, D., Wen, F., Yu, N.: Bootstrapped masked autoencoders for vision bert pretraining. In: European Conference on Computer Vision, pp. 247–264 (2022). Springer
2022
Later among the works it cites.
Touvron, H., Cord, M., Jégou, H.: Deit iii: Revenge of the vit. In: European Conference on Computer Vision, pp. 516–533 (2022). Springer
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Assran, M., Caron, M., Misra, I., Bojanowski, P., Bordes, F., Vincent, P., Joulin, A., Rabbat, M., Ballas, N.: Masked siamese networks for label-efficient learning. In: European Conference on Computer Vision, pp. 456–473 (2022). Springer
2022
Later among the works it cites.
Mumuni, A., Mumuni, F.: Data augmentation: A comprehensive survey of modern approaches. Array 16
2022
Later among the works it cites.
2022
Later among the works it cites.
Min, B., Ross, H., Sulem, E., Veyseh, A.P.B., Nguyen, T.H., Sainz, O., Agirre, E., Heintz, I., Roth, D.: Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys 56
2023
Later among the works it cites.
Thisanke, H., Deshan, C., Chamith, K., Seneviratne, S., Vidanaarachchi, R., Herath, D.: Semantic segmentation using vision transformers: A survey. Engineering Applications of Artificial Intelligence 126
2023
Later among the works it cites.
Yu, J., Yin, H., Xia, X., Chen, T., Li, J., Huang, Z.: Self-supervised learning for recommender systems: A survey. IEEE Transactions on Knowledge and Data Engineering (2023)
2023
Later among the works it cites.
Schiappa, M.C., Rawat, Y.S., Shah, M.: Self-supervised learning for videos: A survey. ACM Computing Surveys 55
2023
Later among the works it cites.
Assran, M., Duval, Q., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., LeCun, Y., Ballas, N.: Self-supervised learning from images with a joint-embedding predictive architecture. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15619–15629 (2023)
2023
Later among the works it cites.
Mo, S., Sun, Z., Li, C.: Multi-level contrastive learning for self-supervised vision transformers. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2778–2787 (2023)
2023
Later among the works it cites.
Fini, E., Astolfi, P., Alahari, K., Alameda-Pineda, X., Mairal, J., Nabi, M., Ricci, E.: Semi-supervised learning made simple with self-supervised clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3187–3197 (2023)
2023
Later among the works it cites.
Fang, X., Zhang, G., Zhang, G., Zhou, X., Wu, J., Zhao, L.: A hybrid self-supervised learning framework for hyperspectral image classification. In: Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology, pp. 1–7 (2023)
2023
Later among the works it cites.
Tukra, S., Hoffman, F., Chatfield, K.: Improving visual representation learning through perceptual understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14486–14495 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Chen, K., Liu, Z., Hong, L., Xu, H., Li, Z., Yeung, D.-Y.: Mixed autoencoder for self-supervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22742–22751 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Xu, M., Yoon, S., Fuentes, A., Park, D.S.: A comprehensive survey of image augmentation techniques for deep learning. Pattern Recognition 137
2023
Later among the works it cites.
Hassan, M., Ali, S., Kim, J.Y., Saadia, A., Sanaullah, M., Alquhayz, H., Safdar, K.: Developing a novel methodology by integrating deep learning and hmm for segmentation of retinal blood vessels in fundus images. Interdisciplinary Sciences: Computational Life Sciences 15
2023
Later among the works it cites.
Self-supervised monocular depth estimation based on combining convolution and multilayer perceptron. Engineering Applications of Artificial Intelligence 117
2023
Later among the works it cites.
Ren, Y.: Intelligent vehicle violation detection system under human–computer interaction and computer vision. International Journal of Computational Intelligence Systems 17
2024
Closest in time.
AI, S.: Scale.com. Accessed: June Monday, 2024 (2024). https://scale.com/pricing
2024
Closest in time.
Gui, J., Chen, T., Zhang, J., Cao, Q., Sun, Z., Luo, H., Tao, D.: A survey on self-supervised learning: Algorithms, applications, and future trends. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)
2024
Closest in time.
Ye, J., Kalra, S., Miri, M.S.: Cluster-based histopathology phenotype representation learning by self-supervised multi-class-token hierarchical vit. Scientific Reports 14
2024
Closest in time.
Nguyen, H.H., Yamagishi, J., Echizen, I.: Exploring self-supervised vision transformers for deepfake detection: A comparative analysis. In: 2024 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–10 (2024). IEEE
2024
Closest in time.
Su, Q., Netchaev, A., Li, H., Ji, S.: Flsl: Feature-level self-supervised learning. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Showrov, A.A., Aziz, M.T., Nabil, H.R., Jim, J.R., Kabir, M.M., Mridha, M., Asai, N., Shin, J.: Generative adversarial networks (gans) in medical imaging: advancements, applications and challenges. IEEE Access (2024)
2024
Closest in time.
Shao, R., Bi, X.-J., Chen, Z.: Hybrid vit-cnn network for fine-grained image classification. IEEE Signal Processing Letters (2024)
2024
Closest in time.
Wu, J., Xu, X.: Eslaxdet: A new x-ray baggage security detection framework based on self-supervised vision transformers. Engineering Applications of Artificial Intelligence 127
2024
Closest in time.
Kalluri, P.R., Agnew, W., Cheng, M., Owens, K., Soldaini, L., Birhane, A.: Computer-vision research powers surveillance technology. Nature, 1–7 (2025)
2025
Closest in time.
Mendes, J., Oliveira, B., Araújo, C., Galrão, J., Garcia, N.C., Matela, N.: You get the best of both worlds? integrating deep learning and traditional machine learning for breast cancer risk prediction. Computers in Biology and Medicine 187
2025
Closest in time.
Bosma, J.S., Dercksen, K., Builtjes, L., André, R., Roest, C., Fransen, S.J., Noordman, C.R., Navarro-Padilla, M., Lefkes, J., Alves, N., et al
2025
Closest in time.
Alipour, P., Gallegos, E.: Leveraging generative ai synthetic and social media data for content generalizability to overcome data constraints in vision deep learning. Artificial Intelligence Review 58
2025
Closest in time.
Dai, D., Zhang, Y., Yang, Q., Xu, L., Shen, X., Xia, S., Wang, G.: Pathologyvlm: a large vision-language model for pathology image understanding. Artificial Intelligence Review 58
2025
Closest in time.
Al-Abri, S., Keshvari, S., Al-Rashdi, K., Al-Hmouz, R., Bourdoucen, H.: Computer vision based approaches for fish monitoring systems: a comprehensive study. Artificial Intelligence Review 58
2025
Closest in time.
González-Almagro, G., Peralta, D., De Poorter, E., Cano, J.-R., García, S.: Semi-supervised constrained clustering: An in-depth overview, ranked taxonomy and future research directions. Artificial Intelligence Review 58
2025
Closest in time.
Hosny, K.M., Mohammed, M.A.: Explainable ai and vision transformers for detection and classification of brain tumor: a comprehensive survey. Artificial Intelligence Review 58
2025
Closest in time.
Wang, W.-Y., Du, W.-W., Xu, D., Wang, W., Peng, W.-C.: A survey on self-supervised learning for non-sequential tabular data. Machine Learning 114
2025
Closest in time.
Upadhyay, A., Chandel, N.S., Singh, K.P., Chakraborty, S.K., Nandede, B.M., Kumar, M., Subeesh, A., Upendar, K., Salem, A., Elbeltagi, A.: Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture. Artificial Intelligence Review 58
2025
Closest in time.
Xu, Y., Khan, T.M., Song, Y., Meijering, E.: Edge deep learning in computer vision and medical diagnostics: a comprehensive survey. Artificial Intelligence Review 58
2025
Closest in time.
Wang, Y., Deng, Y., Zheng, Y., Chattopadhyay, P., Wang, L.: Vision transformers for image classification: A comparative survey. Technologies 13
2025
Closest in time.
Elharrouss, O., Himeur, Y., Mahmood, Y., Alrabaee, S., Ouamane, A., Bensaali, F., Bechqito, Y., Chouchane, A.: Vits as backbones: Leveraging vision transformers for feature extraction. Information Fusion, 102951 (2025)
2025
Closest in time.
Haruna, Y., Qin, S., Chukkol, A.H.A., Yusuf, A.A., Bello, I., Lawan, A.: Exploring the synergies of hybrid convolutional neural network and vision transformer architectures for computer vision: A survey. Engineering Applications of Artificial Intelligence 144
2025
Closest in time.
2025
Closest in time.
Jia, R., Gao, K., Liu, Y., Yu, B., Ma, X., Ma, Z.: i-cltp: Integrated contrastive learning with transformer framework for traffic state prediction and network-wide analysis. Transportation Research Part C: Emerging Technologies 171
2025
Closest in time.
Li, Z., Cui, Z., Zhang, L., Wang, S., Lei, C., Ouyang, X., Chen, D., Zhao, X., Liu, C., Liu, Z., et al
2025
Closest in time.
Hemalatha, K., Vetriselvi, V.: Self-supervised learning using diverse cell images for cervical cancer classification. Measurement 243
2025
Closest in time.
Lin, J., Wu, D., Huang, L.: Self-supervised bi-directional mapping generative adversarial network for arbitrary-time longitudinal interpolation of missing data. Biomedical Signal Processing and Control 105
2025
Closest in time.
Zhao, T., Yue, Y., Sun, H., Li, J., Wen, Y., Yao, Y., Qian, W., Guan, Y., Qi, S.: Maemc-net: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from ct images. Frontiers in Medicine 12
2025
Closest in time.