Leveraging YOLO-World and GPT-4V LMMs for Zero-Shot Person Detection and
  Action Recognition in Drone Imagery
Christian Limberg Artur Gonçalves Bastien Rigault Helmut Prendinger
In this article, we explore the potential of zero-shot Large Multimodal
Models (LMMs) in the domain of drone perception. We focus on person detection
and action recognition tasks and evaluate two prominent LMMs, namely YOLO-World
and GPT-4V(ision) using a publicly available dataset captured from aerial
views. Traditional deep learning approaches rely heavily on large and
high-quality training datasets. However, in certain robotic settings, acquiring
such datasets can be resource-intensive or impractical within a reasonable
timeframe. The flexibility of prompt-based Large Multimodal Models (LMMs) and
their exceptional generalization capabilities have the potential to
revolutionize robotics applications in these scenarios. Our findings suggest
that YOLO-World demonstrates good detection performance. GPT-4V struggles with
accurately classifying action classes but delivers promising results in
filtering out unwanted region proposals and in providing a general description
of the scenery. This research represents an initial step in leveraging LMMs for
drone perception and establishes a foundation for future investigations in this
area.