For all the ways convolutional neural nets have revolutionized computer vision in recent years, one important aspect has received surprisingly little attention: the effect of image size on the accuracy of tasks being trained for. Typically, to be efficient, the input images are resized to a relatively small spatial resolution (e.g. 224x224), and both training and inference are carried out at this resolution. The actual mechanism for this re-scaling has been an afterthought: Namely, off-the-shelf image resizers such as bilinear and bicubic are commonly used in most machine learning software frameworks. But do these resizers limit the on task performance of the trained networks? The answer is yes. Indeed, we show that the typical linear resizer can be replaced with learned resizers that can substantially improve performance. Importantly, while the classical resizers typically result in better perceptual quality of the downscaled images, our proposed learned resizers do not necessarily give better visual quality, but instead improve task performance. Our learned image resizer is jointly trained with a baseline vision model. This learned CNN-based resizer creates machine friendly visual manipulations that lead to a consistent improvement of the end task metric over the baseline model. Specifically, here we focus on the classification task with the ImageNet dataset, and experiment with four different models to learn resizers adapted to each model. Moreover, we show that the proposed resizer can also be useful for fine-tuning the classification baselines for other vision tasks. To this end, we experiment with three different baselines to develop image quality assessment (IQA) models on the AVA dataset.
翻译:对于革命性神经网近年来使计算机视觉发生革命的所有方式来说,一个重要方面受到的注意令人惊讶地很少:图像大小对所培训任务准确性的影响。通常,为了提高效率,输入图像被调整成相对较小的空间分辨率(例如224x224),并且在这个决议中进行训练和推论。这种重新缩放的实际机制是事后思考的:即,大多数机器学习软件框架中通常使用双线和双立方体等现成图像振荡器。但是,这些重塑器是否限制经过训练的网络的任务性能?答案是肯定的。我们显示,典型的线性重塑图像可以被替换成相对较小的空间分辨率分辨率分辨率分辨率分辨率(例如224x2242244),而在本决议中,既进行培训和推导力,又提高降级图像的感知性质量,而我们所拟议的再现的再生图像再造精度则提高任务性能质量。我们所学的图像再生图像再造精度与基线模型共同训练,我们所学的基线级质量模型 。这个基于CNN-Regial的图像网络的典型数据实验可以使机更精确的模型更精确地调整。