FALCON: 未来予測と物体中心の文脈事前学習によるUAV行動認識
FALCON: Future-Aware Learning with Contextual Object-Centric Pretraining for UAV Action Recognition
UAV映像の行動認識に向け、物体検出を事前学習時のみに用いて背景の影響を抑え、物体中心の短期・長期未来再構成を組み合わせた自己教師あり学習手法を提案。
著者: Ruiqi Xian, Xiyang Wu, Tianrui Guan, Xijun Wang, Boqing Gong, Dinesh Manocha
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
We introduce FALCON, a unified self-supervised video pretraining approach for UAV action recognition from raw RGB aerial footage, requiring no additional preprocessing at inference. UAV videos exhibit severe spatial imbalance: large, cluttered backgrounds dominate the field of view, causing reconstruction-based pretraining to waste capacity on uninformative regions and under-learn action-relevant human/object cues. FALCON addresses this by integrating object-aware masked autoencoding with object-centric dual-horizon future reconstruction. Using detections only during pretraining, we construct objectness priors that (i) enforce balanced token visibility during masking and (ii) concentrate reconstruction supervision on action-relevant regions, preventing learning from being dominated by background appearance. To promote temporal dynamics learning, we further reconstruct short- and long-horizon future content within an object-centric supervision region, injecting anticipatory temporal supervision that is robust to noisy aerial context. Across UAV benchmarks, FALCON improves top-1 accuracy by 2.9\% on NEC-Drone and 5.8\% on UAV-Human with a ViT-B backbone, while achieving 2$\times$--5$\times$ faster inference than supervised approaches that rely on heavy test-time augmentation.
