Visual perception is a key component of data visualization. Much prior empirical work uses eye movement as a proxy to understand human visual perception. Diverse apparatus and techniques have been proposed to collect eye movements, but there is still no optimal approach. In this paper, we review 30 prior works for collecting eye movements based on three axes: (1) the tracker technology used to measure eye movements; (2) the image stimulus shown to participants; and (3) the collection methodology used to gather the data. Based on this taxonomy, we employ a webcam-based eyetracking approach using task-specific visualizations as the stimulus. The low technology requirement means that virtually anyone can participate, thus enabling us to collect data at large scale using crowdsourcing: approximately 12,000 samples in total. Choosing visualization images as stimulus means that the eye movements will be specific to perceptual tasks associated with visualization. We use these data to propose a SCANNER DEEPLY, a virtual eyetracker model that, given an image of a visualization, generates a gaze heatmap for that image. We employ a computationally efficient, yet powerful convolutional neural network for our model. We compare the results of our work with results from the DVS model and a neural network trained on the Salicon dataset. The analysis of our gaze patterns enables us to understand how users grasp the structure of visualized data. We also make our stimulus dataset of visualization images available as part of this paper's contribution.

Despite remarkable success in unpaired image-to-image translation, existing systems still require a large amount of labeled images. This is a bottleneck for their real-world applications; in practice, a model trained on labeled CelebA dataset does not work well for test images from a different distribution -- greatly limiting their application to unlabeled images of a much larger quantity. In this paper, we attempt to alleviate this necessity for labeled data in the facial image translation domain. We aim to explore the degree to which you can discover novel attributes from unlabeled faces and perform high-quality translation. To this end, we use prior knowledge about the visual world as guidance to discover novel attributes and transfer them via a novel normalization method. Experiments show that our method trained on unlabeled data produces high-quality translations, preserves identity, and be perceptually realistic, as good as, or better than, state-of-the-art methods trained on labeled data.

Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount of training data. To tackle this issue, we present a novel memory-augmented colorization model MemoPainter that can produce high-quality colorization with limited data. In particular, our model is able to capture rare instances and successfully colorize them. Also, we propose a novel threshold triplet loss that enables unsupervised training of memory networks without the need for class labels. Experiments show that our model has superior quality in both few-shot and one-shot colorization tasks.