  Video scene graph generation (VidSGG) has emerged as a transformative
approach to capturing and interpreting the intricate relationships among
objects and their temporal dynamics in video sequences. In this paper, we
introduce the new AeroEye dataset that focuses on multi-object relationship
modeling in aerial videos. Our AeroEye dataset features various drone scenes
and includes a visually comprehensive and precise collection of predicates that
capture the intricate relationships and spatial arrangements among objects. To
this end, we propose the novel Cyclic Graph Transformer (CYCLO) approach that
allows the model to capture both direct and long-range temporal dependencies by
continuously updating the history of interactions in a circular manner. The
proposed approach also allows one to handle sequences with inherent cyclical
patterns and process object relationships in the correct sequential order.
Therefore, it can effectively capture periodic and overlapping relationships
while minimizing information loss. The extensive experiments on the AeroEye
dataset demonstrate the effectiveness of the proposed CYCLO model,
demonstrating its potential to perform scene understanding on drone videos.
Finally, the CYCLO method consistently achieves State-of-the-Art (SOTA) results
on two in-the-wild scene graph generation benchmarks, i.e., PVSG and ASPIRe.
