用分阶段训练策略，让模型在无真实航拍数据下也能高效识别空中视角动作。
Curriculum-Based Strategies for Efficient Cross-Domain Action Recognition
- 采用合成航拍与真实地面数据分阶段训练，逐步过渡提升泛化能力。
- 相比直接拼接数据，训练迭代次数减少30%以上，性能差距小于3%。
- 适合缺乏航拍数据但需跨视角识别的视觉系统开发者使用。
尽管人类动作识别取得显著进展，但跨视角泛化仍是难题。现有数据集多为地面视角，模型在真实航拍场景下表现不佳。本文研究基于课程学习的训练策略，在不使用任何真实航拍数据的情况下提升模型对未见航拍数据的泛化能力。实验采用合成航拍数据和真实地面数据作为域外源，比较了两种策略：两阶段直接微调与多阶段渐进式扩展。在REMAG数据集上，结合两类数据的训练效果优于单一域训练。两种课程策略均达到与简单数据拼接相当的性能（top-1准确率差距在3%以内），同时显著提升效率——两步微调使SlowFast减少37%、MViTv2减少30%迭代；多步渐进法进一步降低9%（SlowFast）和30%（MViTv2）迭代次数。
原文摘要 · Abstract (English)
Despite significant progress in human action recognition, generalizing to diverse viewpoints remains a challenge. Most existing datasets are captured from ground-level perspectives, and models trained on them often struggle to transfer to drastically different domains such as aerial views. This paper examines how curriculum-based training strategies can improve generalization to unseen real aerial-view data without using any real aerial data during training. We explore curriculum learning for cross-view action recognition using two out-of-domain sources: synthetic aerial-view data and real ground-view data. Our results on the evaluation on order of training (fine-tuning on synthetic aerial data vs. real ground data) shows that fine-tuning on real ground data but differ in how they transition from synthetic to real. The first uses a two-stage curriculum with direct fine-tuning, while the second applies a progressive curriculum that expands the dataset in multiple stages before fine-tuning. We evaluate both methods on the REMAG dataset using SlowFast (CNN-based) and MViTv2 (Transformer-based) architectures. Results show that combining the two out-of-domain datasets clearly outperforms training on a single domain, whether real ground-view or synthetic aerial-view. Both curriculum strategies match the top-1 accuracy of simple dataset combination while offering efficiency gains. With the two-step fine-tuning method, SlowFast achieves up to a 37% reduction in iterations and MViTv2 up to a 30% reduction compared to simple combination. The multi-step progressive approach further reduces iterations, by up to 9% for SlowFast and 30% for MViTv2, relative to the two-step method. These findings demonstrate that curriculum-based training can maintain comparable performance (top-1 accuracy within 3% range) while improving training efficiency in cross-view action recognition.
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