论文标题
摄像机偏见在细粒度的分类任务中
Camera Bias in a Fine Grained Classification Task
论文作者
论文摘要
我们表明，可以通过卷积神经网络（CNN）来利用用于获取图像的相机与该图像的类标签之间的相关性，从而通过识别哪个相机拍摄了图像并从相机中推断出类标签，从而在图像分类任务上“作弊”了一个模型。我们表明，在具有相机 /标签相关性的数据集上训练的模型并不能很好地推广到缺乏这些相关性的图像，也不是来自未经摄像机的图像。此外，我们研究了它们正在利用哪些视觉功能进行相机识别。我们的实验提供了证据反对全球颜色统计，晶状体变形和色差的重要性，并支持高频特征，这可以通过相机内置的图像处理算法引入。
We show that correlations between the camera used to acquire an image and the class label of that image can be exploited by convolutional neural networks (CNN), resulting in a model that "cheats" at an image classification task by recognizing which camera took the image and inferring the class label from the camera. We show that models trained on a dataset with camera / label correlations do not generalize well to images in which those correlations are absent, nor to images from unencountered cameras. Furthermore, we investigate which visual features they are exploiting for camera recognition. Our experiments present evidence against the importance of global color statistics, lens deformation and chromatic aberration, and in favor of high frequency features, which may be introduced by image processing algorithms built into the cameras.
