HMR-Net: Hierarchical Modular Routing for Cross-Domain Object Detection in Aerial Images

2026-04-20

AI Summary

This work addresses the limited generalization capability of object detection in aerial imagery caused by discrepancies in spatial resolution, scene composition, and semantic labeling across domains. To tackle this challenge, the authors propose a hierarchical modular routing framework that integrates geospatial-aware global routing with category-semantic-conditioned expert modules. By jointly leveraging global expert allocation and local scene decomposition, the method enables dual specialization—across datasets and within individual scenes—and supports zero-shot detection of novel categories without fine-tuning. Extensive experiments on four aerial image datasets demonstrate substantial improvements in multi-domain generalization, region-specific detection accuracy, and open-category recognition performance.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Despite advances in object detection, aerial imagery remains a challenging domain, as models often fail to generalize across variations in spatial resolution, scene composition, and semantic label coverage. Differences in geographic context, sensor characteristics, and object distributions across datasets limit the capacity of conventional models to learn consistent and transferable representations. Shared methods trained on such data tend to impose a unified representation across fundamentally different domains, resulting in poor performance on region-specific content and less flexibility when dealing with novel object categories. To address this, we propose a novel modular learning framework that enables structured specialization in aerial detection. Our method introduces a hierarchical routing mechanism with two levels of modularity: a global expert assignment layer that uses latent geographic embeddings to route datasets to specialized processing modules, and a local scene decomposition mechanism that allocates image subregions to region-specific sub-modules. This allows our method to specialize across datasets and within complex scenes. Additionally, the framework contains a conditional expert module that uses external semantic information (e.g., category names or textual descriptions) to enable detection of novel object categories during inference, without the need for retraining or fine-tuning. By moving beyond monolithic representations, our method offers an adaptive framework for remote sensing object detection. Comprehensive evaluations on four datasets highlight improvements in multi-dataset generalization, regional specialization, and open-category detection.

Problem

• cross-domain object detection
• aerial images
• domain generalization
• novel category detection
• remote sensing

Innovation

• Hierarchical Modular Routing
• Cross-Domain Object Detection
• Geographic Embedding
• Conditional Expert Module
• Open-Category Detection