Learning to Segment Every Thing

Most methods for object instance segmentation require all training examples to be labeled with segmentation masks. This requirement makes it expensive to annotate new categories and has restricted instance segmentation models to ~100 well-annotated classes. The goal of this paper is to propose a new partially supervised training paradigm, together with a novel weight transfer function, that enables training instance segmentation models on a large set of categories all of which have box annotations, but only a small fraction of which have mask annotations. These contributions allow us to train Mask R-CNN to detect and segment 3000 visual concepts using box annotations from the Visual Genome dataset and mask annotations from the 80 classes in the COCO dataset. We evaluate our approach in a controlled study on the COCO dataset. This work is a first step towards instance segmentation models that have broad comprehension of the visual world

Individual, social and physical environmental correlates of 'never' and 'always' cycling to school among 10 to 12 year old children living within a 3.0 km distance from school

BACKGROUND: Cycling to school has been identified as an important target for increasing physical activity levels in children. However, knowledge about correlates of cycling to school is scarce as many studies did not make a distinction between walking and cycling to school. Moreover, correlates of cycling to school for those who live within a distance, that in theory would allow cycling to school, stay undiscovered. Therefore, this study examined individual, social and physical environmental correlates of never and always cycling to/from school among 10 to 12 year old Belgian children living within a 3.0 km distance from school. METHODS: 850 parents completed a questionnaire to assess personal, family, behavioral, cognitive, social and physical environmental factors related to the cycling behavior of their children. Parents indicated on a question matrix how many days a week their child (1) walked, (2) cycled, was (3) driven by car or (4) public transport to and from school during fall, winter and spring. Multivariate logistic regression analyses were conducted to examine the correlates. RESULTS: Overall, 39.3% of children never cycled to school and 16.5% of children always cycled to school. Children with high levels of independent mobility and good cycling skills perceived by their parents were more likely to always cycle to school (resp. OR 1.06; 95% CI 1.04-1.15 and OR 1.08; 95% CI 1.01-1.16) and less likely to never cycle to school (resp. OR 0.84; 95% CI 0.78-0.91 and OR 0.77; 95% CI 0.7-0.84). Children with friends who encourage them to cycle to school were more likely to always cycle to school (OR 1.08; 95% CI 1.01-1.15) and less likely to never cycle to school (OR 0.9; 95% CI 0.83-1.0). In addition, children with parents who encourage them to cycle to school were less likely to never cycle to school (OR 0.78; 95% CI 0.7-0.87). Regarding the physical environmental factors, only neighborhood traffic safety was significantly associated with cycling: i.e., children were more likely to always cycle to school if neighborhood traffic was perceived as safe by their parents (OR 1.18; 95% CI 1.07-1.31). CONCLUSION: Individual, social and physical environmental factors were associated with children's cycling behavior to/from school. However, the contribution of the physical environment is limited and highlights the fact that interventions for increasing cycling to school should not focus solely on the physical environment

Forecasting bicycle traffic in cities

In this project the task is to predict bicycle theft and bicycle traffic in a city using machine learning methods. The project proposal was given in collaboration with BikeFinder AS, a Petter Stordalen"s #Strawberry Million” award winning company established in 2015. Bicycle theft is a problem in many places around the world and one of the objectives in this thesis is to help preventing it, based on data science analysis and machine learning methods applied on existing data. Predicting bicycle traffic as well as analyzing the factors that might affect traffic is another important goal for this thesis. However, throughout the project it is expected to work on various other steps such as gathering the relevant data, pre-processing, evaluating and comparing methods and results. It is also important to optimize and improve the performance of the methods to achieve as accurate results as possible. Lastly, interpreting the results, and solving the questions asked in the thesis. The project has been solved by first, gathering BikeFinder theft and traffic data, Stavanger weather conditions data, Rogaland Police District bike theft reports data and data from the bike counting sensors in the city of Stavanger. Secondly, various steps of preprocessing has been done on the data according to the use cases. Afterwards, machine learning method evaluations and comparisons, using a neutral and larger dataset, Chicago crime dataset was accomplished. Thereafter, applying the best performing methods on the theft and traffic datasets, as well as forecasting bike theft and traffic has been achieved. Finally, results interpretation and discussion on the findings of the project. The findings in this project reflects that bike theft and bike traffic can be predicted using machine learning methods on BikeFinder data. Furthermore, other factors such as weather conditions do affect bike traffic as well as improves the performances of bike traffic predictions. The results of the project provide useful insight to multiple parties and can be used to help preventing bike theft as well as providing suggestions for city planning improvements