Subject
Artificial Intelligence,Cognitive Neuroscience
Reference65 articles.
1. Amrani, E., Ari, R. B., Rotman, D., & Bronstein, A. (2021). Noise estimation using density estimation for self-supervised multimodal learning. In Proceedings of the 2021 association for the advancement of artificial intelligence (pp. 6644–6652). Virtual Online, Canada: http://dx.doi.org/10.1609/aaai.v35i8.16822.
2. All grains, one scheme (AGOS): learning multigrain instance representation for aerial scene classification;Bi;IEEE Transactions on Geoscience and Remote Sensing,2022
3. Buch, S., Eyzaguirre, C., Gaidon, A., Wu, J., Li, F., & Niebles, J. C. (2022). Revisiting the “video” in video-language understanding. In Proceedings of the 2022 IEEE conference on computer vision and pattern recognition (pp. 2917–2927). New Orleans, USA: http://dx.doi.org/10.1109/CVPR52688.2022.00293.
4. Cai, J., Yuan, C., Shi, C., Li, L., Cheng, Y., & Shan, Y. (2020). Feature augmented memory with global attention network for videoqa. In Proceedings of the 2020 international joint conference on artificial intelligence (pp. 998–1004). Yokohama, Japan: http://dx.doi.org/10.24963/ijcai.2020/138.
5. Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., & Brox, T. (2014). Discriminative unsupervised feature learning with convolutional neural networks. In Proceedings of the 2014 advances conference on neural information processing systems (pp. 766–774). Montreal, Canada.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献