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While AI technology is becoming increasingly prevalent in our daily lives, the comprehension of machine learning (ML) among non-experts remains limited. Interactive machine learning (IML) has the potential to serve as a tool for end users, but many existing IML systems are designed for users with a certain level of expertise. Consequently, it remains unclear whether IML experiences can enhance the comprehension of ordinary users. In this study, we conducted a public event using an IML system to assess whether participants could gain technical comprehension through hands-on IML experiences. We implemented an interactive sound classification system featuring visualization of internal feature representation and invited visitors at a science museum to freely interact with it. By analyzing user behavior and questionnaire responses, we discuss the potential and limitations of IML systems as a tool for promoting technical comprehension among non-experts., Comment: 26 pages, 9 figures

Interactive machine learning (IML) allows users to build their custom machine learning models without expert knowledge. While most existing IML systems are designed with classification algorithms, they sometimes oversimplify the capabilities of machine learning algorithms and restrict the user's task definition. On the other hand, as recent large-scale language models have shown, natural language representation has the potential to enable more flexible and generic task descriptions. Models that take images as input and output text have the potential to represent a variety of tasks by providing appropriate text labels for training. However, the effect of introducing text labels to IML system design has never been investigated. In this work, we aim to investigate the difference between image-to-text translation and image classification for IML systems. Using our prototype systems, we conducted a comparative user study with non-expert users, where participants solved various tasks. Our results demonstrate the underlying difficulty for users in properly defining image recognition tasks while highlighting the potential and challenges of interactive image-to-text translation systems., Comment: 14 pages, 8 figures

Despite recent advances in appearance-based gaze estimation techniques, the need for training data that covers the target head pose and gaze distribution remains a crucial challenge for practical deployment. This work examines a novel approach for synthesizing gaze estimation training data based on monocular 3D face reconstruction. Unlike prior works using multi-view reconstruction, photo-realistic CG models, or generative neural networks, our approach can manipulate and extend the head pose range of existing training data without any additional requirements. We introduce a projective matching procedure to align the reconstructed 3D facial mesh with the camera coordinate system and synthesize face images with accurate gaze labels. We also propose a mask-guided gaze estimation model and data augmentation strategies to further improve the estimation accuracy by taking advantage of synthetic training data. Experiments using multiple public datasets show that our approach significantly improves the estimation performance on challenging cross-dataset settings with non-overlapping gaze distributions., Comment: Camera-ready version for CVPR 2022 Workshop (GAZE 2022)

Together with the recent advances in semantic segmentation, many domain adaptation methods have been proposed to overcome the domain gap between training and deployment environments. However, most previous studies use limited combinations of source/target datasets, and domain adaptation techniques have never been thoroughly evaluated in a more challenging and diverse set of target domains. This work presents a new multi-domain dataset DRIV100 for benchmarking domain adaptation techniques on in-the-wild road-scene videos collected from the Internet. The dataset consists of pixel-level annotations for 100 videos selected to cover diverse scenes/domains based on two criteria; human subjective judgment and an anomaly score judged using an existing road-scene dataset. We provide multiple manually labeled ground-truth frames for each video, enabling a thorough evaluation of video-level domain adaptation where each video independently serves as the target domain. Using the dataset, we quantify domain adaptation performances of state-of-the-art methods and clarify the potential and novel challenges of domain adaptation techniques. The dataset is available at https://doi.org/10.5281/zenodo.4389243.

Conditional image generation is effective for diverse tasks including training data synthesis for learning-based computer vision. However, despite the recent advances in generative adversarial networks (GANs), it is still a challenging task to generate images with detailed conditioning on object shapes. Existing methods for conditional image generation use category labels and/or keypoints and are only give limited control over object categories. In this work, we present SCGAN, an architecture to generate images with a desired shape specified by an input normal map. The shape-conditioned image generation task is achieved by explicitly modeling the image appearance via a latent appearance vector. The network is trained using unpaired training samples of real images and rendered normal maps. This approach enables us to generate images of arbitrary object categories with the target shape and diverse image appearances. We show the effectiveness of our method through both qualitative and quantitative evaluation on training data generation tasks., Comment: Accepted at ACCV 2018

Previous work on eye tracking and eye-based human-computer interfaces mainly concentrated on making use of the eyes in traditional desktop settings. With the recent growth of interest in smart glass devices and low-cost eye trackers, however, gaze-based techniques for mobile computing is becoming increasingly important. PETMEI 2014 focuses on the pervasive eye tracking paradigm as a trailblazer for mobile eye-based interaction and eye-based context-awareness. We want to stimulate and explore the creativity of these communities with respect to the implications, key research challenges, and new applications for pervasive eye tracking in ubiquitous computing. The long-term goal is to create a strong interdisciplinary research community linking these fields together and to establish the workshop as the premier forum for research on pervasive eye tracking.