Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres

Many computer vision challenges require continuous outputs, but tend to be solved by discrete classification. The reason is classification's natural containment within a probability $n$-simplex, as defined by the popular softmax activation function. Regular regression lacks such a closed geometry, leading to unstable training and convergence to suboptimal local minima. Starting from this insight we revisit regression in convolutional neural networks. We observe many continuous output problems in computer vision are naturally contained in closed geometrical manifolds, like the Euler angles in viewpoint estimation or the normals in surface normal estimation. A natural framework for posing such continuous output problems are $n$-spheres, which are naturally closed geometric manifolds defined in the $\mathbb{R}^{(n+1)}$ space. By introducing a spherical exponential mapping on $n$-spheres at the regression output, we obtain well-behaved gradients, leading to stable training. We show how our spherical regression can be utilized for several computer vision challenges, specifically viewpoint estimation, surface normal estimation and 3D rotation estimation. For all these problems our experiments demonstrate the benefit of spherical regression. All paper resources are available at https://github.com/leoshine/Spherical_Regression.Comment: CVPR 2019 camera read

Motor learning during reaching movements: model acquisition and recalibration

This thesis marks a departure from the traditional task-based distinction between sensorimotor adaptation and skill learning by focusing on the mechanisms that underlie adaptation and skill learning. I argue that adaptation is a recalibration of an existing control policy, whereas skill learning is the acquisition and subsequent automatization of a new control policy. A behavioral criterion to distinguish the two mechanisms is offered. The first empirical chapter contrasts learning in visuomotor rotations of 40° with learning left-right reversals during reaching movements. During left-right reversals, speed-accuracy trade-offs increased and offline gains emerged, whereas during visual rotations, speed-accuracy trade-offs remained constant and instead of offline gains, there was offline forgetting. I argue that these dissociations reflect differences in the underlying learning mechanisms: acquisition and recalibration. The second empirical chapter tests whether the dissociation based on time-accuracy trade-offs reveals a general property of recalibration or whether instead the interpretation is limited to the specific contrast between left-right reversals and visuomotor rotations. When the size of the prediction error– the difference between intended and perceived movement – was gradually increased participants switched from recalibration to control policy acquisition. This switching point can be derived by considering the role of internal models in recalibration: If the internal model that learns from errors and the environment are too dissimilar – e.g. in left-right reversal and large rotations– recalibration would cause the system to learn from errors in the wrong way, such that prediction errors would increase further. To address this problem the final empirical chapter explores if the way the system learns from errors can be reversed. In conclusion, the results provide behavioral criteria to differentiate between adaptation and skill learning. By exploring the boundaries of recalibration this thesis contributes to a more principled understanding of the mechanisms involved in adaptation and skill learning

Examining Occupational Therapy Students’ Responses to Integrative Seminars

The integrative seminar is an innovative teaching-learning approach that focuses on active learning and peer collaboration, characteristics that align with millennial learners’ preferences. The use of integrative seminars has been reported by various health professions with positive outcomes. Course feedback survey data from the first cohort of occupational therapy students who participated in a new four-course integrative seminar series were analyzed. Findings suggest that the format of the courses was engaging for the learners. The students particularly valued the small class; the opportunities for peer collaboration; and the variety of active learning opportunities, including simulations. The students also indicated that the seminars helped them to integrate and apply their learning across the curriculum. In another survey completed near the end of their Level II fieldwork rotations, the students indicated that the seminars contributed to their readiness for fieldwork as well as to the development of their critical thinking, interpersonal skills, and professional identity. The findings from this analysis support the potential value of integrative seminars in occupational therapy education