← Back to all news
arXiv Computer Vision By Xinjie Yao, Ruipu Zhao, Yunqi Zhu, Zhihe Fan, Zhoupeng Guo, Weihao Li, Zhen Wang, Qilong Wang, Pengfei Zhu

Socialized UAV Cross-Task Learning: Towards Cross-Granularity Collaboration through Hierarchical Interaction

Read the original on arXiv Computer Vision →

The paper introduces CrossUAV, a benchmark for joint object detection and instance segmentation in UAV imagery, and proposes Cross-Granularity Socialized Collaboration (CGSC), a framework that regulates hierarchical interactions between tasks. CGSC progressively activates cross-task exchanges and adaptively adjusts interaction strength based on task contribution, aiming to reduce interference and exploit complementary coarse- and fine-grained knowledge. Experiments show consistent improvements on both detection and segmentation tasks, supporting the effectiveness of hierarchical dynamic interaction for cross-granularity collaboration.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv AI
Aug 20

GrabVG: Graph-Attentive Binding for Visual Grounding in UAV Imagery

GrabVG is a visual grounding framework for UAV imagery that tackles the challenges of small, densely packed, and visually similar objects. It splits the task into preattentive hypothesis search and graph‑attentive feature binding, using distillation‑guided proposals and a sparse graph to capture intra‑ and inter‑instance relationships. Experiments on AerialVG and AerialSense show that GrabVG achieves higher accuracy and speed, outperforming baselines by significant margins.

By Chaowei Wang, Yan Di, Jingjun Sun, Baozhe Liu, Jiaxu Tian, Yuheng Li, Guangqian Guo, Shan Gao
efficiencysafety
More like this →
Hugging Face Trending Papers
Aug 19

GrabVG: Graph-Attentive Binding for Visual Grounding in UAV Imagery

GrabVG is a visual grounding framework for UAV imagery that tackles the challenges of small, densely packed, and visually similar objects by separating the task into preattentive hypothesis search and graph-attentive feature binding. It first generates a compact set of reliable object hypotheses using distillation-guided proposal induction and text-aware filtering, then constructs a sparse graph where language-guided visual cues and inter-instance topological relationships are jointly bound and propagated via graph attention. Experiments on AerialVG and AerialSense demonstrate that GrabVG achieves a strong accuracy–speed trade‑off, reaching 67.31% and 80.34% Acc@0.5 and outperforming baselines by 10.55 and 8.76 percentage points.

efficiencysafety
More like this →