Multi-view feature High-order Fusion for Space Weak Object Detection and Segmentation

2026-06-13 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

The Multi-view feature High-order Fusion method (MHF) addresses the challenge of detecting and segmenting weak objects in space images and videos, which often have limited appearance information. MHF develops simple multi-view attentions to generate multi-view features and then aggregates these features by extending traditional low-order fusion to higher orders. This approach enhances a model's ability to capture relevant and complementary information through high-order multi-view feature perception and a recursive task-contribution gated selection process. Designed as a flexible, customizable, and plug-and-play module, MHF is compatible with various multi-view feature representations. Extensive experiments on two new space science datasets and a large-scale satellite video dataset demonstrate that MHF significantly improves vision transformers and convolution-based models, achieving state-of-the-art accuracies across both detection and segmentation tasks. The code will be available on GitHub.

Key takeaway

For computer vision engineers developing models for space imagery or weak object detection, you should consider integrating the Multi-view feature High-order Fusion (MHF) module. This plug-and-play method significantly boosts accuracy for both detection and segmentation tasks by enhancing feature representation. Implementing MHF can improve your existing vision transformers and convolution-based models, especially when dealing with limited appearance information.

Key insights

Multi-view feature High-order Fusion (MHF) improves weak object detection and segmentation by aggregating richer, higher-order multi-view features.

Principles

• Multi-view learning enhances feature representation.
• Higher-order feature fusion captures richer information.
• Gated selection refines multi-view feature aggregation.

Method

MHF develops multi-view attentions to generate features, then aggregates them using high-order fusion. This involves high-order multi-view feature perception and recursive task-contribution gated selection to capture relevant and complementary information for weak objects.

In practice

• Integrate MHF as a plug-and-play module.
• Apply MHF to enhance vision transformers.
• Improve weak object representation in space applications.

Topics

• Multi-view Learning
• Weak Object Detection
• Image Segmentation
• Feature Fusion
• Vision Transformers
• Space Applications

Code references

• Kingdroper/MHF

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.