Appearance-Based Gaze Estimation in the Wild

Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild

A Note on Stable States of Dipolar Systems at Low Temperatures

In the past several years, many important innovations in nanotechnology were made. Today it becomes possible to make nanosize magnetic particles, and development of high storage-density magnetic device is desired. In such a magnetic particle system, dipole interaction plays the main role. In this note, we consider stable states of dipolar systems at low temperature: Some systems show ``antiferromagnetic structure'', and others show magnetic domain structure, depending on lattice shapes.Comment: 5 pages including 5 eps figures, to appear in "Computer Simulation Studies in Condensed Matter Physics XVIII", Eds. D. P. Landau, S. P. Lewis, and H.-B. Sch\"{u}ttler (Springer Verlag, Heidelberg, Berlin

Forecasting User Attention During Everyday Mobile Interactions Using Device-Integrated and Wearable Sensors

Visual attention is highly fragmented during mobile interactions, but the erratic nature of attention shifts currently limits attentive user interfaces to adapting after the fact, i.e. after shifts have already happened. We instead study attention forecasting -- the challenging task of predicting users' gaze behaviour (overt visual attention) in the near future. We present a novel long-term dataset of everyday mobile phone interactions, continuously recorded from 20 participants engaged in common activities on a university campus over 4.5 hours each (more than 90 hours in total). We propose a proof-of-concept method that uses device-integrated sensors and body-worn cameras to encode rich information on device usage and users' visual scene. We demonstrate that our method can forecast bidirectional attention shifts and predict whether the primary attentional focus is on the handheld mobile device. We study the impact of different feature sets on performance and discuss the significant potential but also remaining challenges of forecasting user attention during mobile interactions.Comment: 13 pages, 9 figure

Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze Estimation

Appearance-based gaze estimation has been actively studied in recent years. However, its generalization performance for unseen head poses is still a significant limitation for existing methods. This work proposes a generalizable multi-view gaze estimation task and a cross-view feature fusion method to address this issue. In addition to paired images, our method takes the relative rotation matrix between two cameras as additional input. The proposed network learns to extract rotatable feature representation by using relative rotation as a constraint and adaptively fuses the rotatable features via stacked fusion modules. This simple yet efficient approach significantly improves generalization performance under unseen head poses without significantly increasing computational cost. The model can be trained with random combinations of cameras without fixing the positioning and can generalize to unseen camera pairs during inference. Through experiments using multiple datasets, we demonstrate the advantage of the proposed method over baseline methods, including state-of-the-art domain generalization approaches. The code will be available at https://github.com/ut-vision/Rot-MVGaze.Comment: Accepted by WACV2024. The code will be available at https://github.com/ut-vision/Rot-MVGaz

Finite dipolar hexagonal columns on piled layers of triangular lattice

We have investigated, by the Monte Carlo simulation, spin systems which represent moments of arrayed magnetic nanoparticles interacting with each other only by the dipole-dipole interaction. In the present paper we aim the understanding of finite size effects on the magnetic nanoparticles arrayed in hexagonal columns cut out from the close-packing structures or from those with uniaxial compression. In columns with the genuine close-packing structures, we observe a single vortex state which is also observed previously in finite 2-dimensional systems. On the other hand in the system with the inter-layer distance set $1/\sqrt{2}$ times of the close-packing one, we found ground states which depend on the number of layers. The dependence is induced by a finite size effect and is related to a orientation transition in the corresponding bulk system.Comment: 3 pages, 2 figures. Proceedings of the International Conference on Magnetism 2006 (ICM2006) conference. To appear in a special volume of Journal of Magnetism and Magnetic Material

Image preference estimation with a data-driven approach: A comparative study between gaze and image features

Understanding how humans subjectively look at and evaluate images is an important task for various applications in the field of multimedia interaction. While it has been repeatedly pointed out that eye movements can be used to infer the internal states of humans, not many successes have been reported concerning image understanding. We investigate the possibility of image preference estimation based on a person’s eye movements in a supervised manner in this paper. A dataset of eye movements is collected while the participants are viewing pairs of natural images, and it is used to train image preference label classifiers. The input feature is defined as a combination of various fixation and saccade event statistics, and the use of the random forest algorithm allows us to quantitatively assess how each of the statistics contributes to the classification task. We show that the gaze-based classifier had a higher level of accuracy than metadata-based baseline methods and a simple rule-based classifier throughout the experiments. We also present a quantitative comparison with image-based preference classifiers and discuss the potential and limitations of the gaze-based preference estimator

ION-SENSITIVE FIELD-EFFECT TRANSISTORS WITH INORGANIC GATE OXIDE FOR PH SENSING

Ion-sensitive fieldeffect transistors (ISFET’s) have been fabricated by using silicon fiims on sapphire substrates (SOS). Using this structure Si02, Zr02, and TazO5 films are examined as hydrogenion- sensitive materials, and TazO5 fiim has been found to have the highest pH sensitivity (56 mV/pH) among them. The measured pH sensitivity of this SOS-ISFET’s is compared with the theoretical sensitivity based on the site-binding model of proton dimciation reaction on the metal oxide f i i and good agreement between them is obtained

Humanoid Robot With Turnover Prevention and Self-Weight Compensation

Q-bot is the human-sized carriage robot for lifting heavy weight objects of in-house logistics, such as storehouse and convenience store. The main feature of Q-bot is the adhesion mechanism beneath the foot, called the turnover prevention Universal Vacuum Gripper (in short TP UVG) that holds its body for turnover prevention and self-weight compensation. Turnover prevention is one of the key technologies of in-house logistic robot for effective use of it. Self-weight compensation is another clue for the robot to achieve the labor work in narrow space. TP UVG is achieved both functions by adhering to uneven ground. The other function of Q-bot is multiple objects graspability based on two-sized Universal Vacuum Gripper by dual-armed manipulation. Q-bot also has omnidirectional movability based on mecanum wheels. In this research, we will report on the development of Q-bot and experiments to prevent the robot from falling when it grabs a heavy object while attached to the ground. We also report Q-bot demonstrations of Future Convenience-Store Challenge in the World Robot Summit 2018