Sep 17, 2026 · cs.CVJ/K move · Enter open · S save
Xinjie Yao, Ruipu Zhao, Yunqi Zhu, Zhihe Fan+5
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China · School of Artificial Intelligence, Tianjin University, Tianjin 300350, China · School of Computer Science and Engineering, University of New South Wales, NSW 2052, Australia · School of Sports Training, Tianjin University of Sport, Tianjin 300381, China · School of Automation, Southeast University, Nanjing 210096, China
Joint learning across heterogeneous tasks is often treated as task coupling through feature sharing, distillation, or auxiliary supervision. However, in cross-task learning, mismatched representational and supervisory granularities make such coupling prone to interference, teacher bias, or unidirectional collapse. We argue that cross-granularity learning is fundamentally a problem of hierarchical interaction regulation rather than simple task coupling. This issue is particularly evident in UAV perception, where visual shifts and detection--segmentation objectives naturally form coarse- and fine-grained knowledge sources. To systematically study this problem, we introduce CrossUAV, a UAV benchmark for joint object detection and instance segmentation that provides a unified evaluation platform for cross-granularity task collaboration. To address these challenges, we propose Cross-Granularity Socialized Collaboration (CGSC), a progressive and adaptive framework that regulates when, where, and how tasks exchange information across network hierarchies. CGSC progressively activates cross-task interactions and adaptively adjusts the strength according to task contribution, suppressing harmful interference while exploiting complementary coarse- and fine-grained structures. Extensive experiments demonstrate consistent improvements on both tasks, validating hierarchical dynamic interaction as an effective mechanism for cross-granularity collaboration.