# Learning Task Relatedness in Multi-Task Learning for Images in Context

arXiv: https://arxiv.org/abs/1904.03011 · HTML: https://mlsanity.com/paper/1904.03011/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [Multi-Task Meta Learning: learn how to adapt to unseen tasks](https://mlsanity.com/paper/2210.06989/) (2022)
- [Episodic Multi-Task Learning with Heterogeneous Neural Processes](https://mlsanity.com/paper/2310.18713/) (2023)
- [Saliency-Regularized Deep Multi-Task Learning](https://mlsanity.com/paper/2207.01117/) (2022)
- [Task Grouping for Multilingual Text Recognition](https://mlsanity.com/paper/2210.07423/) (2022)
- [STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map](https://mlsanity.com/paper/2307.03374/) (2023)
- [Visual Attention Prompted Prediction and Learning](https://mlsanity.com/paper/2310.08420/) (2023)
- [On Steering Multi-Annotations per Sample for Multi-Task Learning](https://mlsanity.com/paper/2203.02946/) (2022)
- [Handcrafted Feature-Assisted One-Class Learning for Artist Authentication in Historical Drawings](https://mlsanity.com/paper/2601.11627/) (2026)

## Abstract (arXiv)

Multimedia applications often require concurrent solutions to multiple tasks. These tasks hold clues to each-others solutions, however as these relations can be complex this remains a rarely utilized property. When task relations are explicitly defined based on domain knowledge multi-task learning (MTL) offers such concurrent solutions, while exploiting relatedness between multiple tasks performed over the same dataset. In most cases however, this relatedness is not explicitly defined and the domain expert knowledge that defines it is not available. To address this issue, we introduce Selective Sharing, a method that learns the inter-task relatedness from secondary latent features while the model trains. Using this insight, we can automatically group tasks and allow them to share knowledge in a mutually beneficial way. We support our method with experiments on 5 datasets in classification, regression, and ranking tasks and compare to strong baselines and state-of-the-art approaches showing a consistent improvement in terms of accuracy and parameter counts. In addition, we perform an activation region analysis showing how Selective Sharing affects the learned representation.