# Learning Across Tasks and Domains

## TL;DR

This paper introduces a novel framework for transferring knowledge across different visual tasks and domains, enhancing performance in scenarios with limited supervision and demonstrating effectiveness on depth estimation and segmentation tasks across multiple datasets.

## Contribution

The work presents a new adaptation framework that transfers knowledge across tasks and domains, extending existing domain adaptation methods to cross-task scenarios.

## Key findings

- Effective transfer across tasks and domains demonstrated
- Improved performance on monocular depth estimation and segmentation
- Validated on four diverse datasets

## Abstract

Recent works have proven that many relevant visual tasks are closely related one to another. Yet, this connection is seldom deployed in practice due to the lack of practical methodologies to transfer learned concepts across different training processes. In this work, we introduce a novel adaptation framework that can operate across both task and domains. Our framework learns to transfer knowledge across tasks in a fully supervised domain (e.g., synthetic data) and use this knowledge on a different domain where we have only partial supervision (e.g., real data). Our proposal is complementary to existing domain adaptation techniques and extends them to cross tasks scenarios providing additional performance gains. We prove the effectiveness of our framework across two challenging tasks (i.e., monocular depth estimation and semantic segmentation) and four different domains (Synthia, Carla, Kitti, and Cityscapes).