@inproceedings{BAT-20,
  author = {{Bateni}, Peyman and {Goyal}, Raghav and {Masrani}, Vaden and {Wood}, Frank and {Sigal}, Leonid},
  title = {Improved Few-Shot Visual Classification},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  keywords = {LwLL, Computer Science - Computer Vision and Pattern Recognition},
  year = {2020},
  eid = {arXiv:1912.03432},
  archivePrefix = {arXiv},
  eprint = {1912.03432},
  support = {D3M,LwLL},
  url_Link = {https://openaccess.thecvf.com/content_CVPR_2020/html/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.html},
  url_Paper={http://openaccess.thecvf.com/content_CVPR_2020/papers/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.pdf},
  url_ArXiv={https://arxiv.org/abs/1912.03432},
  abstract={Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data. Most few-shot learning approaches to date have focused on progressively more complex neural feature extractors and classifier adaptation strategies, as well as the refinement of the task definition itself. In this paper, we explore the hypothesis that a simple class-covariance-based distance metric, namely the Mahalanobis distance, adopted into a state of the art few-shot learning approach (CNAPS) can, in and of itself, lead to a significant performance improvement. We also discover that it is possible to learn adaptive feature extractors that allow useful estimation of the high dimensional feature covariances required by this metric from surprisingly few samples. The result of our work is a new "Simple CNAPS" architecture which has up to 9.2% fewer trainable parameters than CNAPS and performs up to 6.1% better than state of the art on the standard few-shot image classification benchmark dataset.}
}

%@inproceedings{WAN-19,
%  title={Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation},
%  author={Wang, R and Scibior, A and Wood F},
%  booktitle={NeurIPS self-driving car workshop},
%  year={2019},
%  archiveprefix = {arXiv},
%  eprint = {1909.09721},
%  support = {D3M},
%  url_Paper = {https://arxiv.org/pdf/1909.09721.pdf},
%  url_ArXiv={https://arxiv.org/abs/1909.09721},
%  abstract={Imitation learning is a promising approach to end-to-end training of autonomous vehicle controllers. Typically the driving process with such approaches is entirely automatic and black-box, although in practice it is desirable to control the vehicle through high-level commands, such as telling it which way to go at an intersection. In existing work this has been accomplished by the application of a branched neural architecture, since directly providing the command as an additional input to the controller often results in the command being ignored. In this work we overcome this limitation by learning a disentangled probabilistic latent variable model that generates the steering commands. We achieve faithful command-conditional generation without using a branched architecture and demonstrate improved stability of the controller, applying only a variational objective without any domain-specific adjustments. On top of that, we extend our model with an additional latent variable and augment the dataset to train a controller that is robust to unsafe commands, such as asking it to turn into a wall. The main contribution of this work is a recipe for building controllable imitation driving agents that improves upon multiple aspects of the current state of the art relating to robustness and interpretability.}
%}

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The result of our work is a new \"Simple CNAPS\" architecture which has up to 9.2% fewer trainable parameters than CNAPS and performs up to 6.1% better than state of the art on the standard few-shot image classification benchmark dataset.","bibtex":"@inproceedings{BAT-20,\n author = {{Bateni}, Peyman and {Goyal}, Raghav and {Masrani}, Vaden and {Wood}, Frank and {Sigal}, Leonid},\n title = {Improved Few-Shot Visual Classification},\n booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},\n keywords = {LwLL, Computer Science - Computer Vision and Pattern Recognition},\n year = {2020},\n eid = {arXiv:1912.03432},\n archivePrefix = {arXiv},\n eprint = {1912.03432},\n support = {D3M,LwLL},\n url_Link = {https://openaccess.thecvf.com/content_CVPR_2020/html/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.html},\n url_Paper={http://openaccess.thecvf.com/content_CVPR_2020/papers/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.pdf},\n url_ArXiv={https://arxiv.org/abs/1912.03432},\n abstract={Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data. Most few-shot learning approaches to date have focused on progressively more complex neural feature extractors and classifier adaptation strategies, as well as the refinement of the task definition itself. In this paper, we explore the hypothesis that a simple class-covariance-based distance metric, namely the Mahalanobis distance, adopted into a state of the art few-shot learning approach (CNAPS) can, in and of itself, lead to a significant performance improvement. We also discover that it is possible to learn adaptive feature extractors that allow useful estimation of the high dimensional feature covariances required by this metric from surprisingly few samples. The result of our work is a new \"Simple CNAPS\" architecture which has up to 9.2% fewer trainable parameters than CNAPS and performs up to 6.1% better than state of the art on the standard few-shot image classification benchmark dataset.}\n}\n\n%@inproceedings{WAN-19,\n% title={Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation},\n% author={Wang, R and Scibior, A and Wood F},\n% booktitle={NeurIPS self-driving car workshop},\n% year={2019},\n% archiveprefix = {arXiv},\n% eprint = {1909.09721},\n% support = {D3M},\n% url_Paper = {https://arxiv.org/pdf/1909.09721.pdf},\n% url_ArXiv={https://arxiv.org/abs/1909.09721},\n% abstract={Imitation learning is a promising approach to end-to-end training of autonomous vehicle controllers. Typically the driving process with such approaches is entirely automatic and black-box, although in practice it is desirable to control the vehicle through high-level commands, such as telling it which way to go at an intersection. In existing work this has been accomplished by the application of a branched neural architecture, since directly providing the command as an additional input to the controller often results in the command being ignored. In this work we overcome this limitation by learning a disentangled probabilistic latent variable model that generates the steering commands. We achieve faithful command-conditional generation without using a branched architecture and demonstrate improved stability of the controller, applying only a variational objective without any domain-specific adjustments. On top of that, we extend our model with an additional latent variable and augment the dataset to train a controller that is robust to unsafe commands, such as asking it to turn into a wall. The main contribution of this work is a recipe for building controllable imitation driving agents that improves upon multiple aspects of the current state of the art relating to robustness and interpretability.}\n%}\n\n","author_short":["Bateni, P.","Goyal, R.","Masrani, V.","Wood, F.","Sigal, L."],"key":"BAT-20","id":"BAT-20","bibbaseid":"bateni-goyal-masrani-wood-sigal-improvedfewshotvisualclassification-2020","role":"author","urls":{" link":"https://openaccess.thecvf.com/content_CVPR_2020/html/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.html"," paper":"http://openaccess.thecvf.com/content_CVPR_2020/papers/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.pdf"," arxiv":"https://arxiv.org/abs/1912.03432"},"keyword":["LwLL","Computer Science - Computer Vision and Pattern Recognition"],"metadata":{"authorlinks":{}},"downloads":11},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/plai-group/bibliography/master/group_publications.bib","creationDate":"2020-06-09T20:47:49.835Z","downloads":11,"keywords":["lwll","computer science - computer vision and pattern recognition"],"search_terms":["improved","few","shot","visual","classification","bateni","goyal","masrani","wood","sigal"],"title":"Improved Few-Shot Visual Classification","year":2020,"dataSources":["7avRLRrz2ifJGMKcD","BKH7YtW7K7WNMA3cj","wyN5DxtoT6AQuiXnm"]}