# UAV-OVO: Out-of-Viewpoint Generalization in UAV Action Recognition Source: [UAV-OVO: Out-of-Viewpoint Generalization in UAV Action Recognition]() Collection index: [llms.txt]() Data updated: 2026-05-27T05:00:21.000Z Authors: Yu Xia, Zhengbo Zhang, Shuaihu Zhang, Zhigang Tu Published: 2026-05-25T09:15:08.000Z ## Abstract UAV action recognition faces a deployment shift that standard benchmarks often obscure: a model trained on UAV footage captured from low-depression viewpoints may be required to recognize the same action classes from high-depression viewpoints. While the action labels remain unchanged, this shift alters body visibility, motion projection, and scene context, encouraging models to rely on viewpoint-specific shortcuts. We introduce UAV-OVO, an Out-of-Viewpoint generalization benchmark for UAV action recognition. UAV-OVO derives view scores from uncalibrated videos, uses a view-isolation band to assign low-depression videos to the training and in-distribution test splits while reserving high-depression videos for out-of-distribution testing, and constructs ID/OOD test sets matched by class distribution so that performance differences reflect viewpoint shift rather than label imbalance. Across representative video recognizers, UAV-OVO reveals a substantial ID/OOD gap: models that fit the low-depression training distribution well often fail to transfer to held-out high-depression views, exposing viewpoint shortcuts hidden by aggregate accuracy. We further propose LATER, LoRA-Anchored Test-time Re-centering, which first adapts the recognizer with Low-Rank Adaptation (LoRA) and then uses the learned LoRA subspace as a semantic anchor for online feature re-centering. Specifically, LATER projects target-domain displacement onto the orthogonal complement of the LoRA subspace before re-centering features, reducing viewpoint-induced drift while preserving task-relevant semantics. Together, UAV-OVO and LATER provide a controlled testbed and a practical adaptation method for viewpoint-robust UAV video understanding. ## 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. - [Action Recognition on UAV-OVO (OOD)](): Action Recognition; UAV-OVO (OOD); Accuracy (OOD): 35.55. - [Action Recognition on UAV-OVO (ID)](): Action Recognition; UAV-OVO (ID); Accuracy (ID): 67.06. ## Original sources and implementation [Original paper]() [PDF]()