论文标题
进行视觉跟踪的序列级训练
Towards Sequence-Level Training for Visual Tracking
论文作者
论文摘要
尽管机器学习在视觉对象跟踪的任务上进行了广泛采用，但最近基于学习的方法在很大程度上忽略了视觉跟踪是其本质上的序列级任务的事实。他们在很大程度上依赖框架级训练，这不可避免地会导致数据分布和任务目标的培训和测试之间的不一致。这项工作介绍了基于强化学习的视觉跟踪序列训练策略，并讨论了数据采样，学习目标和数据增强的序列级设计如何提高跟踪算法的准确性和鲁棒性。我们对包括LASOT，TrackingNet和GoT-10K在内的标准基准测试的实验表明，四个代表性跟踪模型，SiamRPN ++，Siamattn，Transt和TRDIMP，通过不修改建筑架构而始终如一地将所提出的方法纳入训练中。
Despite the extensive adoption of machine learning on the task of visual object tracking, recent learning-based approaches have largely overlooked the fact that visual tracking is a sequence-level task in its nature; they rely heavily on frame-level training, which inevitably induces inconsistency between training and testing in terms of both data distributions and task objectives. This work introduces a sequence-level training strategy for visual tracking based on reinforcement learning and discusses how a sequence-level design of data sampling, learning objectives, and data augmentation can improve the accuracy and robustness of tracking algorithms. Our experiments on standard benchmarks including LaSOT, TrackingNet, and GOT-10k demonstrate that four representative tracking models, SiamRPN++, SiamAttn, TransT, and TrDiMP, consistently improve by incorporating the proposed methods in training without modifying architectures.
