8 accepted papers
In visual place recognition accurately identifying and matching images of locations under varying environmental conditions and viewpoints remains a significant challenge. In this paper we introduce a new technique called Bag-of-Queries (BoQ) which learns a set of global queries designed to capture u…
Despite the remarkable progress of deep learning models for semantic segmentation, the success of these models is strongly limited by the following aspects: 1) large datasets with pixel-level annotations must be available and 2) training must be performed with all classes simultaneously. Indeed, in…
One crucial objective of multi-task learning is to align distributions across tasks so that the information between them can be transferred and shared. However, existing approaches only focused on matching the marginal feature distribution while ignoring the semantic information, which may hinder th…
Grasping is a fundamental robotic task needed for the deployment of household robots or furthering warehouse automation. However, few approaches are able to perform grasp detection in real time (frame rate). To this effect, we present Grasp Quality Spatial Transformer Network (GQ-STN), a one-shot gr…
The higher-order partial least squares (HOPLS) is considered as the state-of-the-art tensor-variate regression modeling for predicting a tensor response from a tensor input. However, the standard HOPLS can quickly become computationally prohibitive or merely impossible, especially when huge and time…
A deep Gaussian process (DGP) is a deep network in which each layer is modelled with a Gaussian process (GP). It is a flexible model that can capture highly-nonlinear functions for complex data sets. However, the network structure of DGP often makes inference computationally expensive. In this paper…
One of the major goals for mobile robots is to be able to traverse any kind of terrains. A possible way to achieve this goal is by the use of legged robots, as they have increased mobility. However, this would require them to be able to modify their gaits, based on the identification of the terrain…
Tensor-variate regression approaches have been spotlighted over the past years, due to the fact that many challenging regression tasks in the real world involve in high-order tensorial data. However, these approaches are often computationally prohibitive, which limits the predictive performance for…