Our daily life is surrounded by textual information. Nowadays, the automatic collection of textual information becomes possible owing to the drastic improvement of scene text detectors and recognizer. The purpose of this paper is to conduct a large-scale survey of co-occurrence between visual objects (such as book and car) and scene texts with a large image dataset and a state-of-the-art scene text detector and recognizer. Especially, we focus on the function of ``label'' texts, which are attached to objects for detailing the objects. By analyzing co-occurrence between objects and scene texts, it is possible to observe the statistics about the label texts and understand how the scene texts will be useful for recognizing the objects and vice versa.

Neural networks have become a powerful tool in pattern recognition and part of their success is due to generalization from using large datasets. However, unlike other domains, time series classification datasets are often small. In order to address this problem, we propose a novel time series data augmentation called guided warping. While many data augmentation methods are based on random transformations, guided warping exploits the element alignment properties of Dynamic Time Warping (DTW) and shapeDTW, a high-level DTW method based on shape descriptors, to deterministically warp sample patterns. In this way, the time series are mixed by warping the features of a sample pattern to match the time steps of a reference pattern. Furthermore, we introduce a discriminative teacher in order to serve as a directed reference for the guided warping. We evaluate the method on all 85 datasets in the 2015 UCR Time Series Archive with a deep convolutional neural network (CNN) and a recurrent neural network (RNN). The code with an easy to use implementation can be found at https://github.com/uchidalab/time_series_augmentation.

In deep metric learning targeted at time series, the correlation between feature activations may be easily enlarged through highly nonlinear neural networks, leading to suboptimal embedding effectiveness. An effective solution to this problem is whitening. For example, in online signature verification, whitening can be derived for three individual Gaussian distributions, namely the distributions of local features at all temporal positions 1) for all signatures of all subjects, 2) for all signatures of each particular subject, and 3) for each particular signature of each particular subject. This study proposes a unified method called total whitening that integrates these individual Gaussians. Total whitening rectifies the layout of multiple individual Gaussians to resemble a standard normal distribution, improving the balance between intraclass invariance and interclass discriminative power. Experimental results demonstrate that total whitening achieves state-of-the-art accuracy when tested on online signature verification benchmarks.