Marquette University Slavic Institute Papers NO. 19

https://epublications.marquette.edu/mupress-book/1010/thumbnail.jp

On the Importance of Visual Context for Data Augmentation in Scene Understanding

Performing data augmentation for learning deep neural networks is known to be important for training visual recognition systems. By artificially increasing the number of training examples, it helps reducing overfitting and improves generalization. While simple image transformations can already improve predictive performance in most vision tasks, larger gains can be obtained by leveraging task-specific prior knowledge. In this work, we consider object detection, semantic and instance segmentation and augment the training images by blending objects in existing scenes, using instance segmentation annotations. We observe that randomly pasting objects on images hurts the performance, unless the object is placed in the right context. To resolve this issue, we propose an explicit context model by using a convolutional neural network, which predicts whether an image region is suitable for placing a given object or not. In our experiments, we show that our approach is able to improve object detection, semantic and instance segmentation on the PASCAL VOC12 and COCO datasets, with significant gains in a limited annotation scenario, i.e. when only one category is annotated. We also show that the method is not limited to datasets that come with expensive pixel-wise instance annotations and can be used when only bounding boxes are available, by employing weakly-supervised learning for instance masks approximation.Comment: Updated the experimental section. arXiv admin note: substantial text overlap with arXiv:1807.0742

Micromagnetic modelling of anisotropic damping in ferromagnet

We report a numerical implementation of the Landau-Lifshitz-Baryakhtar theory, which dictates that the micromagnetic relaxation term obeys the symmetry of the magnetic crystal, i. e. replacing the single intrinsic damping constant with a tensor of corresponding symmetry. The effect of anisotropic relaxation is studied in thin saturated ferromagnetic disk and ellipse with and without uniaxial magneto-crystalline anisotropy. We investigate the angular dependency of the linewidth of magnonic resonances with respect to the given structure of the relaxation tensor. The simulations suggest that the anisotropy of the magnonic linewidth is determined by only two factors: the projection of the relaxation tensor onto the plane of precession and the ellipticity of the later.Comment: 6 pages, 5 figures, submitted to PRB Rapid. Com

BlitzNet: A Real-Time Deep Network for Scene Understanding

Real-time scene understanding has become crucial in many applications such as autonomous driving. In this paper, we propose a deep architecture, called BlitzNet, that jointly performs object detection and semantic segmentation in one forward pass, allowing real-time computations. Besides the computational gain of having a single network to perform several tasks, we show that object detection and semantic segmentation benefit from each other in terms of accuracy. Experimental results for VOC and COCO datasets show state-of-the-art performance for object detection and segmentation among real time systems

Tehnologija kao društveni problem

Tematski blok: Društvene implikacije visoke tehnologij