论文标题
域自适应转移学习在视觉注意力方面意识到数据增强，以进行细粒度的视觉分类
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization
论文作者
论文摘要
细粒度的视觉分类（FGVC）是计算机视觉中的一个具有挑战性的话题。这是一个问题，其特征是较大的阶层内差异和细微的阶层差异。在本文中，我们以弱监督的方式解决了这个问题，在这种情况下，通过视觉注意机制，使用数据增强技术向神经网络模型馈送了其他数据。我们通过基础网络模型进行微调执行域自适应知识转移。我们对六个具有挑战性且常用的FGVC数据集进行了实验，并通过使用注意力吸引的数据增强技术具有从深度学习模型InceptionV3中得出的功能，在大型数据集中预先训练，通过使用注意力吸引数据增强技术来提高准确性。我们的方法在多个FGVC数据集上优于竞争对手方法，并在其他数据集上显示了竞争结果。实验研究表明，可以通过基于视觉注意的数据增强来有效地利用大规模数据集的转移学习，这可以在几个FGVC数据集中获得最新的结果。我们对我们的实验进行了全面分析。我们的方法达到了最新的方法，可导致多个细粒度的分类数据集，包括挑战Cub200-2011 Bird，Flowers-102和FGVC-Aircrafts数据集。
Fine-Grained Visual Categorization (FGVC) is a challenging topic in computer vision. It is a problem characterized by large intra-class differences and subtle inter-class differences. In this paper, we tackle this problem in a weakly supervised manner, where neural network models are getting fed with additional data using a data augmentation technique through a visual attention mechanism. We perform domain adaptive knowledge transfer via fine-tuning on our base network model. We perform our experiment on six challenging and commonly used FGVC datasets, and we show competitive improvement on accuracies by using attention-aware data augmentation techniques with features derived from deep learning model InceptionV3, pre-trained on large scale datasets. Our method outperforms competitor methods on multiple FGVC datasets and showed competitive results on other datasets. Experimental studies show that transfer learning from large scale datasets can be utilized effectively with visual attention based data augmentation, which can obtain state-of-the-art results on several FGVC datasets. We present a comprehensive analysis of our experiments. Our method achieves state-of-the-art results in multiple fine-grained classification datasets including challenging CUB200-2011 bird, Flowers-102, and FGVC-Aircrafts datasets.
