@proceedings {7070,
	title = {ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis},
	year = {2021},
	url = {https://arxiv.org/abs/2108.08827},
	author = {Patrick Esser and Robin Rombach and Andreas Blattmann and Bj{\"o}rn Ommer}
}
@article {7073,
	title = {Improving Deep Metric Learning by Divide and Conquer},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
	year = {2021},
	abstract = {Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far from another. The target similarity on the training data is defined by user in form of ground-truth class labels. However, while the embedding space learns to mimic the user-provided similarity on the training data, it should also generalize to novel categories not seen during training. Besides user-provided groundtruth training labels, a lot of additional visual factors (such as viewpoint changes or shape peculiarities) exist and imply different notions of similarity between objects, affecting the generalization on the images unseen during training. However, existing approaches usually directly learn a single embedding space on all available training data, struggling to encode all different types of relationships, and do not generalize well. We propose to build a more expressive representation by jointly splitting the embedding space and the data hierarchically into smaller sub-parts. We successively focus on smaller subsets of the training data, reducing its variance and learning a different embedding subspace for each data subset. Moreover, the subspaces are learned jointly to cover not only the intricacies, but the breadth of the data as well. Only after that, we build the final embedding from the subspaces in the conquering stage. The proposed algorithm acts as a transparent wrapper that can be placed around arbitrary existing DML methods. Our approach significantly improves upon the state-of-the-art on image retrieval, clustering, and re-identification tasks evaluated using CUB200-2011, CARS196, Stanford Online Products, In-shop Clothes, and PKU VehicleID datasets.},
	url = {https://arxiv.org/abs/2109.04003},
	author = {Sanakoyeu, Artsiom and Pingchuan Ma and Tschernezki, V. and Bj{\"o}rn Ommer}
}
@conference {7068,
	title = {iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis},
	booktitle = {Proceedings of the International Conference on Computer Vision (ICCV)},
	year = {2021},
	url = {https://arxiv.org/abs/2107.02790},
	author = {Andreas Blattmann and Timo Milbich and Michael Dorkenwald 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}
}
