A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results. Not surprisingly, the manual design process becomes a particularly challenging barrier, as it demands sufficient prior experience, enormous effort, intuition, and perhaps some good luck. Meanwhile, neural architecture search has gaining grounds in practical applications as a promising method in tackling the issue of automated search of feasible network structures. In this work, we propose a novel cell-level differentiable architecture search mechanism with early stopping to automate the network design of the tracking module, aiming to adapt backbone features to the objective of Siamese tracking networks during offline training. Besides, the proposed early stopping strategy avoids over-fitting and performance collapse problems leading to generalization improvement. The proposed approach is simple, efficient, and with no need to stack a series of modules to construct a network. Our approach is easy to be incorporated into existing trackers, which is empirically validated using different differentiable architecture search-based methods and tracking objectives. Extensive experimental evaluations demonstrate the superior performance of our approach over five commonly-used benchmarks.
翻译:现在,一个强大的视觉物体跟踪器依靠其精心设计的模块,这些模块通常包括人工设计的网络结构,以提供高质量的跟踪结果。毫不奇怪,手工设计过程成为一个特别具有挑战性的障碍,因为它需要足够的先前经验、巨大的努力、直觉和或许是一些幸运。与此同时,神经结构搜索在实际应用方面已获得越来越多的依据,作为解决可行网络结构自动搜索问题的有希望的方法。在这项工作中,我们建议建立一个新型的细胞级差异性建筑搜索机制,及早停止将跟踪模块的网络设计自动化,目的是在离线培训期间使主干特征适应西亚米斯跟踪网络的目标。此外,拟议的早期停止战略避免了超时和性能崩溃问题,导致总体化的改进。拟议方法简单、高效,不需要堆叠一系列模块来构建网络。我们的方法很容易被纳入现有的跟踪器,而现有的跟踪器则使用不同的建筑搜索方法和跟踪目标,经过经验验证,广泛实验性评估表明我们的方法优于五个常用的基准。