SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation

We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal, from which we can directly extract instance segmentation results. Important to the effectiveness of SGPN is its novel representation of 3D instance segmentation results in the form of a similarity matrix that indicates the similarity between each pair of points in embedded feature space, thus producing an accurate grouping proposal for each point. To the best of our knowledge, SGPN is the first framework to learn 3D instance-aware semantic segmentation on point clouds. Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results. We also demonstrate its flexibility by seamlessly incorporating 2D CNN features into the framework to boost performance

Erkenntnisse der neueren Unterrichtsforschung zum Fremdsprachenunterricht

Nachdem dem Fremdsprachenunterricht in der empirischen Schulleistungsforschung lange Zeit eher geringe Aufmerksam zuteil wurde, liefern Studien wie LAU oder DESI mittlerweile wichtige Befunde zu den Schulleistungen und der Unterrichtsqualität im Fremdsprachenbereich. Im vorliegenden Beitrag soll anhand von ausgewählten Beispielen ein knapper Einblick in Fragestellungen, Forschungsdesigns und Befunde jüngerer empirischer Bildungsforschung zum Fremdsprachenunterricht gegeben werden

Erkenntnisse der neueren Unterrichtsforschung zum Fremdsprachenunterricht

Nachdem dem Fremdsprachenunterricht in der empirischen Schulleistungsforschung lange Zeit eher geringe Aufmerksam zuteil wurde, liefern Studien wie LAU oder DESI mittlerweile wichtige Befunde zu den Schulleistungen und der Unterrichtsqualität im Fremdsprachenbereich. Im vorliegenden Beitrag soll anhand von ausgewählten Beispielen ein knapper Einblick in Fragestellungen, Forschungsdesigns und Befunde jüngerer empirischer Bildungsforschung zum Fremdsprachenunterricht gegeben werden

Stochastic Dynamics for Video Infilling

In this paper, we introduce a stochastic dynamics video infilling (SDVI) framework to generate frames between long intervals in a video. Our task differs from video interpolation which aims to produce transitional frames for a short interval between every two frames and increase the temporal resolution. Our task, namely video infilling, however, aims to infill long intervals with plausible frame sequences. Our framework models the infilling as a constrained stochastic generation process and sequentially samples dynamics from the inferred distribution. SDVI consists of two parts: (1) a bi-directional constraint propagation module to guarantee the spatial-temporal coherence among frames, (2) a stochastic sampling process to generate dynamics from the inferred distributions. Experimental results show that SDVI can generate clear frame sequences with varying contents. Moreover, motions in the generated sequence are realistic and able to transfer smoothly from the given start frame to the terminal frame. Our project site is https://xharlie.github.io/projects/project_sites/SDVI/video_results.htmlComment: Winter Conference on Applications of Computer Vision (WACV 2020