# Cross-connected Networks for Multi-task Learning of Detection and Segmentation

arXiv: https://arxiv.org/abs/1805.05569 · HTML: https://mlsanity.com/paper/1805.05569/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2018.

## Built on by (most cited first, 4 of 4)

- [Ladder Siamese Network: a Method and Insights for Multi-level Self-Supervised Learning](https://mlsanity.com/paper/2211.13844/) (2022)
- [Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Condition](https://mlsanity.com/paper/2505.24001/) (2025)
- [M&M: Multimodal-Multitask Model Integrating Audiovisual Cues in Cognitive Load Assessment](https://mlsanity.com/paper/2403.09451/) (2024)
- [Cross-talk based multi-task learning for fault classification of machine system influenced by multiple variables](https://mlsanity.com/paper/2602.05146/) (2026)

## Abstract (arXiv)

Multi-task learning improves generalization performance by sharing knowledge among related tasks. Existing models are for task combinations annotated on the same dataset, while there are cases where multiple datasets are available for each task. How to utilize knowledge of successful single-task CNNs that are trained on each dataset has been explored less than multi-task learning with a single dataset. We propose a cross-connected CNN, a new architecture that connects single-task CNNs through convolutional layers, which transfer useful information for the counterpart. We evaluated our proposed architecture on a combination of detection and segmentation using two datasets. Experiments on pedestrians show our CNN achieved a higher detection performance compared to baseline CNNs, while maintaining high quality for segmentation. It is the first known attempt to tackle multi-task learning with different training datasets between detection and segmentation. Experiments with wild birds demonstrate how our CNN learns general representations from limited datasets.