@conference {sayed:GCPR:2018,
	title = {Cross and Learn: Cross-Modal Self-Supervision},
	booktitle = {German Conference on Pattern Recognition (GCPR) (Oral)},
	year = {2018},
	address = {Stuttgart, Germany},
	abstract = {In this paper we present a self-supervised method to learn feature representations for different modalities. Based on the observation that cross-modal information has a high semantic meaning we propose a method to effectively exploit this signal. For our method we utilize video data since it is available on a large scale and provides easily accessible modalities given by RGB and optical flow. We demonstrate state-of-the-art performance on highly contested action recognition datasets in the context of self-supervised learning. We also show the transferability of our feature representations and conduct extensive ablation studies to validate our core contributions.},
	keywords = {action recognition, cross-modal, image understanding, unsupervised learning},
	url = {https://arxiv.org/abs/1811.03879v1},
	author = {Sayed, N. and Biagio Brattoli and Bj{\"o}rn Ommer}
}
@conference {buechler:ECCV:2018,
	title = {Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning},
	booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
	year = {2018},
	publisher = {(UB and BB  contributed equally)},
	organization = {(UB and BB  contributed equally)},
	address = {Munich, Germany},
	abstract = {Self-supervised learning of convolutional neural networks can harness large amounts of cheap unlabeled data to train powerful feature representations. As surrogate task, we jointly address ordering of visual data in the spatial and temporal domain. The permutations of training samples, which are at the core of self-supervision by ordering, have so far been sampled randomly from a fixed preselected set. Based on deep reinforcement learning we propose a sampling policy that adapts to the state of the network, which is being trained. Therefore, new permutations are sampled according to their expected utility for updating the convolutional feature representation. Experimental evaluation on unsupervised and transfer learning tasks demonstrates competitive performance on standard benchmarks for image and video classification and nearest neighbor retrieval.},
	keywords = {action recognition, deep reinforcement learning, image understanding, self-supervision, shuffling},
	author = {Uta B{\"u}chler and Biagio Brattoli and Bj{\"o}rn Ommer}
}
