1 research outputs found
Cross-connected Networks for Multi-task Learning of Detection and Segmentation
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