The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …

In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

The goal of multi-task learning is to enable more efficient learning than single task learning
by sharing model structures for a diverse set of tasks. A standard multi-task learning …

Multi-task learning can leverage information learned by one task to benefit the training of
other tasks. Despite this capacity, naively training all tasks together in one model often …

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 …

Multi-object tracking (MOT) is an important problem in computer vision which has a wide
range of applications. Formulating MOT as multi-task learning of object detection and re-ID …

With the advent of deep learning, many dense prediction tasks, ie, tasks that produce pixel-
level predictions, have seen significant performance improvements. The typical approach is …

We demonstrate that models trained only in simulation can be used to solve a manipulation
problem of unprecedented complexity on a real robot. This is made possible by two key …

Previous multi-task dense prediction methods based on the Mixture of Experts (MoE) have
received great performance but they neglect the importance of explicitly modeling the global …

We propose a novel multi-task learning architecture, which allows learning of task-specific
feature-level attention. Our design, the Multi-Task Attention Network (MTAN), consists of a …