标题： 红外目标检测与超小卷积网络：ImageNet预训练是否仍然有用？ 显示英文标题

标题： Infrared Object Detection with Ultra Small ConvNets: Is ImageNet Pretraining Still Useful?

摘要： 许多现实世界的应用需要在不同操作条件和模态下具有鲁棒性的识别模型，但同时又要在硬件有限的小型嵌入式设备上运行。 虽然对于正常大小的模型，预训练在准确性和鲁棒性方面已被证明非常有益，但对于可以用于嵌入式和边缘设备的小型模型，其效果尚不明确。 在本工作中，我们研究了ImageNet预训练对越来越小的主干架构（超小型模型，具有$<$1M 参数）在红外视觉模态下的下游目标检测任务中的鲁棒性影响。 利用来自标准目标识别架构的缩放定律，我们构建了两个超小型主干家族，并系统地研究了它们的性能。 我们在三个不同的数据集上的实验表明，尽管ImageNet预训练仍然有用，但超过一定容量阈值后，它在分布外检测鲁棒性方面的收益会逐渐减少。 因此，我们建议实践者仍然使用预训练，并在可能的情况下避免过于小型的模型，因为虽然它们可能在域内问题上表现良好，但在工作条件不同时却很脆弱。 显示英文摘要

摘要： Many real-world applications require recognition models that are robust to different operational conditions and modalities, but at the same time run on small embedded devices, with limited hardware. While for normal size models, pre-training is known to be very beneficial in accuracy and robustness, for small models, that can be employed for embedded and edge devices, its effect is not clear. In this work, we investigate the effect of ImageNet pretraining on increasingly small backbone architectures (ultra-small models, with $<$1M parameters) with respect to robustness in downstream object detection tasks in the infrared visual modality. Using scaling laws derived from standard object recognition architectures, we construct two ultra-small backbone families and systematically study their performance. Our experiments on three different datasets reveal that while ImageNet pre-training is still useful, beyond a certain capacity threshold, it offers diminishing returns in terms of out-of-distribution detection robustness. Therefore, we advise practitioners to still use pre-training and, when possible avoid too small models as while they might work well for in-domain problems, they are brittle when working conditions are different.