@proceedings {7004,
	title = {Unsupervised Part Discovery by Unsupervised Disentanglement},
	year = {2020},
	month = {09/2020},
	address = {T{\"u}bingen},
	abstract = {We address the problem of discovering part segmentations of articulated objects without supervision. In contrast to keypoints, part segmentations provide information about part localizations on the level of individual pixels. Capturing both locations and semantics, they are an attractive target for supervised learning approaches. However, large annotation costs limit the scalability of supervised algorithms to other object categories than humans. Unsupervised approaches potentially allow to use much more data at a lower cost. Most existing unsupervised approaches focus on learning abstract representations to be refined with supervision into the final representation. Our approach leverages a generative model consisting of two disentangled representations for an object{\textquoteright}s shape and appearance and a latent variable for the part segmentation. From a single image, the trained model infers a semantic part segmentation map. In experiments, we compare our approach to previous state-of-the-art approaches and observe significant gains in segmentation accuracy and shape consistency. Our work demonstrates the feasibility to discover semantic part segmentations without supervision.},
	url = {https://compvis.github.io/unsupervised-part-segmentation/},
	author = {Sandro Braun and Patrick Esser and Bj{\"o}rn Ommer}
}
@conference {6323,
	title = {Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis},
	booktitle = {Proceedings of the Intl. Conf. on Computer Vision (ICCV)},
	year = {2019},
	abstract = {Deep generative models come with the promise to learn an explainable representation for visual objects that allows image sampling, synthesis, and selective modification. The main challenge is to learn to properly model the independent latent characteristics of an object, especially its appearance and pose. We present a novel approach that learns disentangled representations of these characteristics and explains them individually. Training requires only pairs of images depicting the same object appearance, but no pose annotations. We propose an additional classifier that estimates the minimal amount of regularization required to enforce disentanglement. Thus both representations together can completely explain an image while being independent of each other. Previous methods based on adversarial approaches fail to enforce this independence, while methods based on variational approaches lead to uninformative representations. In experiments on diverse object categories, the approach successfully recombines pose and appearance to reconstruct and retarget novel synthesized images. We achieve significant improvements over state-of-the-art methods which utilize the same level of supervision, and reach performances comparable to those of pose-supervised approaches. However, we can handle the vast body of articulated object classes for which no pose models/annotations are available.},
	url = {https://compvis.github.io/robust-disentangling/},
	author = {Patrick Esser and Johannes Haux and Bj{\"o}rn Ommer}
}
