Fetching the paper…
Reading the bibliography…
Few-shot learning allows machines to classify novel classes using only a few labeled samples.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., and Darrell, T · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., and Wierstra, D · 2016
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions
Yu, F. and Koltun, V · 2016
Earlier work this paper cites.
One-shot learning for semantic segmentation
Amirreza Shaban, Shray Bansal, Z. L. I. E. and Boots, B · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Earlier work this paper cites.
Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J · 2017
Cited alongside, same era.
Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H · 2018
Cited alongside, same era.
Reptile: a scalable metalearning algorithm
Nichol, A. and Schulman, J · 2018
Cited alongside, same era.
Repmet: Representative-based metric learning for classification and few-shot object detection
Karlinsky, L., Shtok, J., Harary, S., Schwartz, E., Aides, A., Feris, R., Giryes, R., and Bronstein, A. M · 2019
Later among the works it cites.
Feature weighting and boosting for few-shot segmentation
Nguyen, K. and Todorovic, S · 2019
Later among the works it cites.
Panet: Few-shot image semantic segmentation with prototype alignment
Wang, K., Liew, J. H., Zou, Y., Zhou, D., and Feng, J · 2019
Later among the works it cites.
Tapnet: Neural network augmented with task-adaptive projection for few-shot learning
Yoon, S. W., Seo, J., and Moon, J · 2019
Later among the works it cites.
Prior guided feature enrichment network for few-shot segmentation
Tian, Z., Zhao, H., Shu, M., Yang, Z., Li, R., and Jia, J · 2020
Later among the works it cites.
Few-shot semantic segmentation with democratic attention networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B. N., Lacoste, A., and Rodriguez, P · 2018
Cited alongside, same era.
Conditional networks for few-shot semantic segmentation
Rakelly, K., Shelhamer, E., Darrell, T., Efros, A., and Levine, S · 2018
Cited alongside, same era.
Meta-learning with latent embedding optimization
Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R · 2018
Cited alongside, same era.
Meta-ssd: Towards fast adaptation for few-shot object detection with meta-learning
Fu, K., Zhang, T., Zhang, Y., Yan, M., Chang, Z., Zhang, Z., and Sun, X · 2019
Cited alongside, same era.
Task agnostic meta-learning for few-shot learning
Jamal, M. A. and Qi, G.-J · 2019
Cited alongside, same era.
Few-shot object detection via feature reweighting
Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., and Darrell, T · 2019
Cited alongside, same era.
Wang, H., Zhang, X., Hu, Y., Yang, Y., Cao, X., and Zhen, X · 2020
Later among the works it cites.
Prototype mixture models for few-shot semantic segmentation
Yang, B., Liu, C., Li, B., Jiao, J., and Ye, Q · 2020
Later among the works it cites.
Sg-one: Similarity guidance network for one-shot semantic segmentation
Zhang, X., Wei, Y., Yang, Y., and Huang, T. S · 2020
Later among the works it cites.
Adaptive prototype learning and allocation for few-shot segmentation
Li, G., Jampani, V., Sevilla-Lara, L., Sun, D., Kim, J., and Kim, J · 2021
Later among the works it cites.
Simpler is better: Few-shot semantic segmentation with classifier weight transformer
Lu, Z., He, S., Zhu, X., Zhang, L., Song, Y.-Z., and Xiang, T · 2021
Later among the works it cites.
Learning meta-class memory for few-shot semantic segmentation
Wu, Z., Shi, X., Lin, G., and Cai, J · 2021
Later among the works it cites.