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UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree
KacperKania, MaciejZieba, TomaszKajdanowicz....
Published date-12/01/2020
3DShapeReconstruction
Signed distance field (SDF) is a prominent implicit representation of 3D meshes. Methods that are based on such representation achieved state-of-the-art 3D shape reconstruction quality. However, these methods struggle to …
A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation
RihuanKe, AngelicaAviles-Rivero, SaurabhPandey....
Published date-12/01/2020
SemanticSegmentation, Semi-SupervisedSemanticSegmentation
Semantic segmentation has been widely investigated in the community, in which the state of the art techniques are based on supervised models. Those models have reported unprecedented performance at the …
Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks
ZixuanKe, BingLiu, XingchangHuang....
Published date-12/01/2020
ContinualLearning, TransferLearning
Existing research on continual learning of a sequence of tasks focused on dealing with catastrophic forgetting, where the tasks are assumed to be dissimilar and have little shared knowledge. Some …
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina
CorneliusSchröder, DavidKlindt, SarahStrauss....
Published date-12/01/2020
Visual processing in the retina has been studied in great detail at all levels such that a comprehensive picture of the retina's cell types and the many neural circuits they …
Grabber: A tool to improve convergence in interactive image segmentation
JordãoBragantini, BrunoMoura, AlexandreXavierFalcão....
Published date-12/01/2020
SemanticSegmentation
Interactive image segmentation has considerably evolved from techniques that do not learn the parameters of the model to methods that pre-train a model and adapt it from user inputs during …
Robust compressed sensing using generative models
AjilJalal, LiuLiu, AlexandrosG.Dimakis....
Published date-12/01/2020
We consider estimating a high dimensional signal in $\R^n$ using a sublinear number of linear measurements. In analogy to classical compressed sensing, here we assume a generative model as a …