"Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation"

Student-teacher framework that expands aerial detection to unlimited object categories

CastDet, proposed in this paper, enables aerial object detection without predefined categories using student-teacher learning

https://arxiv.org/abs/2411.02057

🎯 Original Problem:

Current aerial object detection systems can only detect pre-defined categories and need extensive labeled data. They struggle with novel objects and can't handle diverse orientations in aerial imagery.

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🔍 Solution in this Paper:

→ CastDet: A student-teacher framework combining three key components:

- Student model for both horizontal and oriented object detection

- Localization teacher generating high-quality object proposals

- RemoteCLIP as external teacher providing classification knowledge

→ Uses dynamic label queue to maintain and update pseudo-labels during training

→ Implements specialized box selection strategies considering scale and orientation

→ Extends framework to handle oriented object detection with tailored algorithms

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💡 Key Insights:

→ First work to tackle open-vocabulary aerial object detection with orientation handling

→ Novel box selection strategies improve pseudo-label quality

→ Dynamic label queue mechanism enhances training stability

→ Integration of RemoteCLIP provides aerial-specific knowledge

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📊 Results:

→ Tested on multiple aerial detection datasets showing significant improvements

→ First benchmark established for open-vocabulary oriented aerial detection

→ Outperforms baseline approaches in both horizontal and oriented detection tasks