This work is an extenion of DOVESEI (https://arxiv.org/abs/2308.11471), where we improved on the prompt generation and engineering inside DOVESEI, and improved on merging segmentation by stacking target and non-target segmemnntations. The objective was to generate prompts that are dynamic, such that prompts are adaptive to observed images instead of a static prompt.
Details about DOVESEI: https://github.com/MISTLab/DOVESEI/blob/main/README.md
PEACE enables the possibility of dynamically generating prompts that are specifically optimized for an input image. We believe that this is an important step towards developing more robust autonomous UAV systems. In summary, our main contributions are:
- Dynamic aerial prompt engineering per image frame that can adapt to changing environments during safe-landing zone segmentation.
Comparison of CLIP and CLIPSeg’s original prompt engineering and PEACE using images from CARLA.
a) A photo of grass
b) A blurry photo of grass in autumn
c) A photo of grass
d) A 3D photo of grass in morning
docker run --runtime nvidia -it --rm --network=host --volume="$HOME/.Xauthority:/root/.Xauthority:rw" -e DISPLAY=$DISPLAY -v $(pwd):/home haechanmarkbong/blabberseg
git clone --recurse-submodules https://github.com/MISTLab/PEACE.git
sudo apt-get update
sudo apt-get upgrade
colcon build --symlink-install --packages-select ros2_satellite_aerial_view_simulator ros2_open_voc_landing_heatmap ros2_open_voc_landing_heatmap_srv
source install/setup.bash
./experiements.bash
For more information about PEACE, refer to our paper: https://arxiv.org/abs/2310.00085