Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve
  Aerial Visual Perception?
Aritra Dutta Srijan Das Jacob Nielsen Rajatsubhra Chakraborty Mubarak Shah
Despite the commercial abundance of UAVs, aerial data acquisition remains
challenging, and the existing Asia and North America-centric open-source UAV
datasets are small-scale or low-resolution and lack diversity in scene
contextuality. Additionally, the color content of the scenes, solar-zenith
angle, and population density of different geographies influence the data
diversity. These two factors conjointly render suboptimal aerial-visual
perception of the deep neural network (DNN) models trained primarily on the
ground-view data, including the open-world foundational models.
  To pave the way for a transformative era of aerial detection, we present
Multiview Aerial Visual RECognition or MAVREC, a video dataset where we record
synchronized scenes from different perspectives -- ground camera and
drone-mounted camera. MAVREC consists of around 2.5 hours of industry-standard
2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million
annotated bounding boxes. This makes MAVREC the largest ground and aerial-view
dataset, and the fourth largest among all drone-based datasets across all
modalities and tasks. Through our extensive benchmarking on MAVREC, we
recognize that augmenting object detectors with ground-view images from the
corresponding geographical location is a superior pre-training strategy for
aerial detection. Building on this strategy, we benchmark MAVREC with a
curriculum-based semi-supervised object detection approach that leverages
labeled (ground and aerial) and unlabeled (only aerial) images to enhance the
aerial detection. We publicly release the MAVREC dataset:
https://mavrec.github.io.