Estimating the pose of an uncooperative spacecraft is an important computer vision problem
for enabling the deployment of automatic vision-based systems in orbit, with applications …

Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

We investigate multi-task learning approaches that use a shared feature representation for
all tasks. To better understand the transfer of task information, we study an architecture with …

In this paper, we are proposing a unified and principled method for both the querying and
training processes in deep batch active learning. We are providing theoretical insights from …

Multi-Task Learning (MTL) has achieved success in various fields. However, how to balance
different tasks to achieve good performance is a key problem. To achieve the task balancing …

Training a single model on multiple input domains and/or output tasks allows for
compressing information from multiple sources into a unified backbone hence improves …

Multimodal sensors (visual, non-visual, and wearable) can provide complementary
information to develop robust perception systems for recognizing activities accurately …

Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for
predicting a related target domain. Hence, a crucial aspect is to properly combine different …

We propose a new principle for transfer learning, based on a straightforward intuition: if two
domains are similar to each other, the model trained on one domain should also perform …