A Key to Effective Multi-task Learning: Separate Query Selection for Task-Synergized Handling and Node Utilization

In the realm of computer vision, effectively handling multi-tasks simultaneously presents a challenge that necessitates innovative solutions. To better address multiple vision problems, we introduce SeTano, an integrated Graph Neural Network (GNN)-based framework. This framework comprises a Dynamic Edge-Sensing GNN (DES-GNN) backbone, which can dynamically adjust edges to extract more pivotal features, and a downstream design which includes a node reduction and a separate query selection strategy. To validate our approach, we perform multi-task experiments on the ImageNet and MS COCO datasets. The results indicate that the integrated design of SeTano leads to enhanced performance in various vision multi-tasks.