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 …

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 …

Many computer vision applications require solving multiple tasks in real-time. A neural
network can be trained to solve multiple tasks simultaneously using multi-task learning. This …

We propose an end-to-end Multitask Learning Transformer framework, named MulT, to
simultaneously learn multiple high-level vision tasks, including depth estimation, semantic …

The advent of large-scale training has produced a cornucopia of powerful visual recognition
models. However, generative models, such as GANs, have traditionally been trained from …

Multitask learning (MTL) aims to learn multiple tasks simultaneously while exploiting their
mutual relationships. By using shared resources to simultaneously calculate multiple …

Contrastive visual pretraining based on the instance discrimination pretext task has made
significant progress. Notably, recent work on unsupervised pretraining has shown to surpass …

Understanding the structure of multiple related tasks allows for multi-task learning to improve
the generalisation ability of one or all of them. However, it usually requires training each …

In the context of multi-task learning, neural networks with branched architectures have often
been employed to jointly tackle the tasks at hand. Such ramified networks typically start with …