3 accepted papers
People continuously perceive and interact with their surroundings based on underlying intentions that drive their exploration and behaviors. While research in egocentric user and scene understanding has focused primarily on motion and contact-based interaction, forecasting human visual perception it…
Sound can convey significant information for spatial reasoning in our daily lives. To endow deep networks with such ability, we address the challenge of dense indoor prediction with sound in both 2D and 3D via cross-modal knowledge distillation. In this work, we propose a Spatial Alignment via Match…
In spite of recent enormous success of deep convolutional networks in object detection, they require a large amount of bounding box annotations, which are often time-consuming and error-prone to obtain. To make better use of given limited labels, we propose a novel object detection approach that tak…