Author name cluster

Mikhail Usvyatsov

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3 papers

2 author rows

JMLR Journal 2022 Journal Article

tntorch: Tensor Network Learning with PyTorch

Mikhail Usvyatsov
Rafael Ballester-Ripoll
Konrad Schindler

We present tntorch, a tensor learning framework that supports multiple decompositions (including Candecomp/Parafac, Tucker, and Tensor Train) under a unified interface. With our library, the user can learn and handle low-rank tensors with automatic differentiation, seamless GPU support, and the convenience of PyTorch's API. Besides decomposition algorithms, tntorch implements differentiable tensor algebra, rank truncation, cross-approximation, batch processing, comprehensive tensor arithmetics, and more. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2022. ( edit, beta )

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IROS Conference 2020 Conference Paper

Indoor Scene Recognition in 3D

Shengyu Huang
Mikhail Usvyatsov
Konrad Schindler

Recognising in what type of environment one is located is an important perception task. For instance, for a robot operating indoors it is helpful to be aware whether it is in a kitchen, a hallway or a bedroom. Existing approaches attempt to classify the scene based on 2D images or 2. 5D range images. Here, we study scene recognition from 3D point cloud (or voxel) data, and show that it greatly outperforms methods based on 2D birds-eye views. Moreover, we advocate multi-task learning as a way to improve scene recognition, building on the fact that the scene type is highly correlated with the objects in the scene, and therefore with its semantic segmentation into different object classes. In a series of ablation studies, we show that successful scene recognition is not just the recognition of individual objects unique to some scene type (such as a bathtub), but depends on several different cues, including coarse 3D geometry, colour, and the (implicit) distribution of object categories. Moreover, we demonstrate that surprisingly sparse 3D data is sufficient to classify indoor scenes with good accuracy.

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ICRA Conference 2019 Conference Paper

Visual recognition in the wild by sampling deep similarity functions

Mikhail Usvyatsov
Konrad Schindler

Recognising relevant objects or object states in its environment is a basic capability for an autonomous robot. The dominant approach to object recognition in images and range images is classification by supervised machine learning, nowadays mostly with deep convolutional neural networks (CNNs). This works well for target classes whose variability can be completely covered with training examples. However, a robot moving in the wild, i. e. , in an environment that is not known at the time the recognition system is trained, will often face domain shift: the training data cannot be assumed to exhaustively cover all the within-class variability that will be encountered in the test data. In that situation, learning is in principle possible, since the training set does capture the defining properties, respectively dissimilarities, of the target classes. But directly training a CNN to predict class probabilities is prone to overfitting to irrelevant correlations between the class labels and the specific subset of the target class that is represented in the training set. We explore the idea to instead learn a Siamese CNN that acts as similarity function between pairs of training examples. Class predictions are then obtained by measuring the similarities between a new test instance and the training samples. We show that the CNN embedding correctly recovers the relative similarities to arbitrary class exemplars in the training set. And that therefore few, randomly picked training exemplars are sufficient to achieve good predictions, making the procedure efficient.

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