# LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection

Source: [LEAP:D -- A Novel Prompt-based Approach for Domain-Generalized Aerial Object Detection](<https://www.sota2.com/research/paper/68fa75af1e209131cea6948c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:11.029Z

Authors: Chanyeong Park, Heegwang Kim, Joonki Paik

Published: 2024-11-14T04:39:10.000Z

## Abstract

Drone-captured images present significant challenges in object detection due to varying shooting conditions, which can alter object appearance and shape. Factors such as drone altitude, angle, and weather cause these variations, influencing the performance of object detection algorithms. To tackle these challenges, we introduce an innovative vision-language approach using learnable prompts. This shift from conventional manual prompts aims to reduce domain-specific knowledge interference, ultimately improving object detection capabilities. Furthermore, we streamline the training process with a one-step approach, updating the learnable prompt concurrently with model training, enhancing efficiency without compromising performance. Our study contributes to domain-generalized object detection by leveraging learnable prompts and optimizing training processes. This enhances model robustness and adaptability across diverse environments, leading to more effective aerial object detection.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2411.09180>)

[PDF](<https://arxiv.org/pdf/2411.09180.pdf>)