Scene sketch semantic segmentation is a crucial task for various applications including sketch-to-image retrieval and scene understanding. Existing sketch segmentation methods treat sketches as bitmap images, leading to the loss of temporal order among strokes due to the shift from vector to image format. Moreover, these methods struggle to segment objects from categories absent in the training data. In this paper, we propose a Class-Agnostic Visio-Temporal Network (CAVT) for scene sketch semantic segmentation. CAVT employs a class-agnostic object detector to detect individual objects in a scene and groups the strokes of instances through its post-processing module. This is the first approach that performs segmentation at both the instance and stroke levels within scene sketches. Furthermore, there is a lack of free-hand scene sketch datasets with both instance and stroke-level class annotations. To fill this gap, we collected the largest Free-hand Instance-and Stroke-level Scene Sketch Dataset (FrISS) that contains 1K scene sketches and covers 403 object classes with dense annotations. Extensive experiments on FrISS and other datasets demonstrate the superior performance of our method over state-of-the-art scene sketch segmenttion models. Our code and dataset can be accessed from https://github.com/aleynakutuk6/CAVT.

Drawings are powerful means of pictorial abstraction and communication. Understanding diverse forms of drawings, including digital arts, cartoons, and comics, has been a major problem of interest for the computer vision and computer graphics communities. Although there are large amounts of digitized drawings from comic books and cartoons, they contain vast stylistic variations, which necessitate expensive manual labeling for training domain-specific recognizers. In this work, we show how self-supervised learning, based on a teacher-student network with a modified student network update design, can be used to build face and body detectors. Our setup allows exploiting large amounts of unlabeled data from the target domain when labels are provided for only a small subset of it. We further demonstrate that style transfer can be incorporated into our learning pipeline to bootstrap detectors using a vast amount of out-of-domain labeled images from natural images (i.e., images from the real world). Our combined architecture yields detectors with state-of-the-art (SOTA) and near-SOTA performance using minimal annotation effort. Our code can be accessed from https://github.com/barisbatuhan/DASS_Detector.

Developing learning analytics dashboards (LADs) is a growing research interest as online learning tools have become more accessible in K-12 and higher education settings. This paper reports our multimodal classroom engagement data analysis and dashboard design process and the resulting engagement dashboard. Our work stems from the importance of monitoring classroom engagement, which refers to students' active physical and cognitive involvement in learning that influences their motivation and success in a given course. To monitor this vital facade of learning, we developed an engagement dashboard using an iterative and user-centered process. We first created a multimodal machine learning model that utilizes face and pose features obtained from recent deep learning models. Then, we created a dashboard where users can view their engagement over time and discover their learning/teaching patterns. Finally, we conducted user studies with undergraduate and graduate-level participants to obtain feedback on our dashboard design. Our paper makes three contributions by (1) presenting a student-centric, open-source dashboard, (2) demonstrating a baseline architecture for engagement analysis using our open-access data, and (3) presenting user insights and design takeaways to inspire future LADs. We expect our research to guide the development of tools for novice teacher education, student self-evaluation, and engagement evaluation in crowded classrooms.

Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human–computer interaction research. In recent years, machines have managed to master the art of generating speech that is understandable by humans. However, the linguistic content of an utterance encompasses only a part of its meaning. Affect, or expressivity, has the capacity to turn speech into a medium capable of conveying intimate thoughts, feelings, and emotions—aspects that are essential for engaging and naturalistic interpersonal communication. While the goal of imparting expressivity to synthesized utterances has so far remained elusive, following recent advances in text-to-speech synthesis, a paradigm shift is well under way in the fields of affective speech synthesis and conversion as well. Deep learning, as the technology that underlies most of the recent advances in artificial intelligence, is spearheading these efforts. In this overview, we outline ongoing trends and summarize state-of-the-art approaches in an attempt to provide a broad overview of this exciting field.

Due to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry (e.g.,How are you?) might induce responses with different affects depending on the affective state of the conversational partner(s) and the context of the conversation. Yet, most dialog systems do not consider affect as constitutive aspect of response generation. In this article, we introduceAffectON, an approach for generating affective responses during inference. For generating language in a targeted affect, our approach leverages a probabilistic language model and an affective space.AffectONis language model agnostic, since it can work with probabilities generated by any language model (e.g., sequence-to-sequence models, neural language models, n-grams). Hence, it can be employed for both affective dialog and affective language generation. We experimented with affective dialog generation and evaluated the generated text objectively and subjectively. For the subjective part of the evaluation, we designed a custom user interface for rating and provided recommendations for the design of such interfaces. The results, both subjective and objective demonstrate that our approach is successful in pulling the generated language toward the targeted affect, with little sacrifice in syntactic coherence.

Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method does not require a statistical body shape model. We exploit Variational Autoencoders to develop a novel framework capable of transitioning from a simple 2D stick figure sketch, to a corresponding 3D human model. Our network learns the mapping between the input sketch and the output 3D model. Furthermore, our model learns the embedding space around these models. We demonstrate that our network can generate not only 3D models, but also 3D animations through interpolation and extrapolation in the learned embedding space. In addition to 3D human models, we produce 3D horse models in order to show the generalization ability of our framework. Extensive experiments show that our model learns to generate compatible 3D models and animations with 2D sketches.

Programming has become a core subject in primary and middle school curricula. Yet, conventional solutions for in-class programming activities require each student to have expensive equipment, which creates an opportunity gap for low-income students. Paper programming can provide an affordable, engaging, and collaborative in-class programming experience by allowing groups of students to use inexpensive materials and share smartphones. However, current paper-programming examples are limited in terms of language expressivity and generalizability. Addressing these limitations, we developed a paper-programming flow and its variants in different abstraction levels and input/output styles. The programming environments consist of pre-defined tangible programming cards and a mobile application that runs computer vision models to recognize them. This paper describes our educational and technical development process, presents a qualitative analysis of the early user study results and shares our design considerations to help develop wide-reaching paper programming environments.

A key aspect of social human-robot interaction is natural non-verbal communication. In this work, we train an agent with batch reinforcement learning to generate nods and smiles as backchannels in order to increase the naturalness of the interaction and to engage humans. We introduce the Sequential Random Deep Q-Network (SRDQN) method to learn a policy for backchannel generation, that explicitly maximizes user engagement. The proposed SRDQN method outperforms the existing vanilla Q-learning methods when evaluated using off-policy policy evaluation techniques. Furthermore, to verify the effectiveness of SRDQN, a human-robot experiment has been designed and conducted with an expressive 3d robot head. The experiment is based on a story-shaping game designed to create an interactive social activity with the robot. The engagement of the participants during the interaction is computed from user's social signals like backchannels, mutual gaze and adjacency pair. The subjective feedback from participants and the engagement values strongly indicate that our framework is a step forward towards the autonomous learning of a socially acceptable backchanneling behavior.

We propose a speech-driven laughter backchannel generation model to reward engagement during human-agent interaction. We formulate the problem as a Markov decision process where speech signal represents the state and the objective is to maximize human engagement. Since online training is often impractical in the case of human-agent interaction, we utilize the existing human-to-human dyadic interaction datasets to train our agent for the backchannel generation task. We address the problem using an actor-critic method based on conservative Q-learning (CQL), that mitigates the distributional shift problem by suppressing Q-value over-estimation during training. The proposed CQL based approach is evaluated objectively on the IEMOCAP dataset for laughter generation task. When compared to the existing off-policy Q-learning methods, we observe an improved compliance with the dataset in terms of laugh generation rate. Furthermore, we show the effectiveness of the learned policy by estimating the expected engagement using off-policy policy evaluation techniques.

Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method requires neither a statistical body shape model nor a rigged 3D character model. We exploit Variational Autoencoders to develop a novel framework capable of transitioning from a simple 2D stick figure sketch, to a corresponding 3D human model. Our network learns the mapping between the input sketch and the output 3D model. Furthermore, our model learns the embedding space around these models. We demonstrate that our network can generate not only 3D models, but also 3D animations through interpolation and extrapolation in the learned embedding space. Extensive experiments show that our model learns to generate reasonable 3D models and animations.

The ability to generate appropriate verbal and nonverbal backchannels by an agent during human-robot interaction greatly enhances the interaction experience. Backchannels are particularly important in applications like tutoring and counseling, which require constant attention and engagement of the user. We present here a method for training a robot for backchannel generation during a human-robot interaction within the reinforcement learning (RL) framework, with the goal of maintaining high engagement level. Since online learning by interaction with a human is highly time-consuming and impractical, we take advantage of the recorded human-to-human dataset and approach our problem as a batch reinforcement learning problem. The dataset is utilized as a batch data acquired by some behavior policy. We perform experiments with laughs as a backchannel and train an agent with value-based techniques. In particular, we demonstrate the effectiveness of recurrent layers in the approximate value function for this problem, that boosts the performance in partially observable environments. With off-policy policy evaluation, it is shown that the RL agents are expected to produce more engagement than an agent trained from imitation learning.

We present a novel method for training a social robot to generate backchannels during human-robot interaction. We address the problem within an off-policy reinforcement learning framework, and show how a robot may learn to produce non-verbal backchannels like laughs, when trained to maximize the engagement and attention of the user. A major contribution of this work is the formulation of the problem as a Markov decision process (MDP) with states defined by the speech activity of the user and rewards generated by quantified engagement levels. The problem that we address falls into the class of applications where unlimited interaction with the environment is not possible (our environment being a human) because it may be time-consuming, costly, impracticable or even dangerous in case a bad policy is executed. Therefore, we introduce deep Q-network (DQN) in a batch reinforcement learning framework, where an optimal policy is learned from a batch data collected using a more controlled policy. We suggest the use of human-to-human dyadic interaction datasets as a batch of trajectories to train an agent for engaging interactions. Our experiments demonstrate the potential of our method to train a robot for engaging behaviors in an offline manner.

“Serious games” are becoming extremely relevant to individuals who have specific needs, such as children with an autism spectrum condition (ASC). Often, individuals with an ASC have difficulties in interpreting verbal and nonverbal communication cues during social interactions. The ASC-Inclusion EU-FP7 funded project aims to provide children who have an ASC with a platform to learn emotion expression and recognition, through play in the virtual world. In particular, the ASC-Inclusion platform focuses on the expression of emotion via facial, vocal, and bodily gestures. The platform combines multiple analysis tools, using onboard microphone and webcam capabilities. The platform utilizes these capabilities via training games, text-based communication, animations, video, and audio clips. This paper introduces current findings and evaluations of the ASC-Inclusion platform and provides detailed description for the different modalities.

We present HapTable; a multi-modal interactive tabletop that allows users to interact with digital images and objects through natural touch gestures, and receive visual and haptic feedback accordingly. In our system, hand pose is registered by an infrared camera and hand gestures are classified using a Support Vector Machine (SVM) classifier. To display a rich set of haptic effects for both static and dynamic gestures, we integrated electromechanical and electrostatic actuation techniques effectively on tabletop surface of HapTable, which is a surface capacitive touch screen. We attached four piezo patches to the edges of tabletop to display vibrotactile feedback for static gestures. For this purpose, the vibration response of the tabletop, in the form of frequency response functions (FRFs), was obtained by a laser Doppler vibrometer for 84 grid points on its surface. Using these FRFs, it is possible to display localized vibrotactile feedback on the surface for static gestures. For dynamic gestures, we utilize the electrostatic actuation technique to modulate the frictional forces between finger skin and tabletop surface by applying voltage to its conductive layer. To our knowledge, this hybrid haptic technology is one of a kind and has not been implemented or tested on a tabletop. It opens up new avenues for gesture-based haptic interaction not only on tabletop surfaces but also on touch surfaces used in mobile devices with potential applications in data visualization, user interfaces, games, entertainment, and education. Here, we present two examples of such applications, one for static and one for dynamic gestures, along with detailed user studies. In the first one, user detects the direction of a virtual flow, such as that of wind or water, by putting their hand on the tabletop surface and feeling a vibrotactile stimulus traveling underneath it. In the second example, user rotates a virtual knob on the tabletop surface to select an item from a menu while feeling the knob's detents and resistance to rotation in the form of frictional haptic feedback.

This paper addresses the problem of evaluating engagement of the human participant by combining verbal and nonverbal behaviour along with contextual information. This study will be carried out through four different corpora. Four different systems designed to explore essential and complementary aspects of the JOKER system in terms of paralinguistic/linguistic inputs were used for the data collection. An annotation scheme dedicated to the labeling of verbal and non-verbal behavior have been designed. From our experiment, engagement in HRI should be multifaceted.

This work advances our understanding of children's visualization literacy, and aims to improve it through a novel approach for teaching visualization at elementary school. We first contribute an analysis of data graphics and activities employed in grade K to 4 educational materials, and the results of a survey conducted with 16 elementary school teachers. We find that visualization education could benefit from integrating pedagogical strategies for teaching abstract concepts with established interactive visualization techniques. Building on these insights, we develop and study design principles for novel interactive teaching material aimed at increasing children's visualization literacy. We specifically contribute C'est La Vis, an online platform for teachers and students to respectively teach and learn about pictographs and bar charts, and report on our initial observations of its use in grades K and 2.

We address the problem of continuous laughter detection over audio-facial input streams obtained from naturalistic dyadic conversations. We first present meticulous annotation of laughters, cross-talks and environmental noise in an audio-facial database with explicit 3D facial mocap data. Using this annotated database, we rigorously investigate the utility of facial information, head movement and audio features for laughter detection. We identify a set of discriminative features using mutual information-based criteria, and show how they can be used with classifiers based on support vector machines (SVMs) and time delay neural networks (TDNNs). Informed by the analysis of the individual modalities, we propose a multimodal fusion setup for laughter detection using different classifier-feature combinations. We also effectively incorporate bagging into our classification pipeline to address the class imbalance problem caused by the scarcity of positive laughter instances. Our results indicate that a combination of TDNNs and SVMs lead to superior detection performance, and bagging effectively addresses data imbalance. Our experiments show that our multimodal approach supported by bagging compares favorably to the state of the art in presence of detrimental factors such as cross-talk, environmental noise, and data imbalance.

Thanks to a remarkably great ability to show amusement and engagement, laughter is one of the most important social markers in human interactions. Laughing together can actually help to set up a positive atmosphere and favors the creation of new relationships. This paper presents a data collection of social interaction dialogs involving humor between a human participant and a robot. In this work, interaction scenarios have been designed in order to study social markers such as laughter. They have been implemented within two automatic systems developed in the Joker project: a social dialog system using paralinguistic cues and a task-based dialog system using linguistic content. One of the major contributions of this work is to provide a context to study human laughter produced during a human-robot interaction. The collected data will be used to build a generic intelligent user interface which provides a multimodal dialog system with social communication skills including humor and other informal socially oriented behaviors. This system will emphasize the fusion of verbal and non-verbal channels for emotional and social behavior perception, interaction and generation capabilities.

In this paper, we introduce a novel stylus capable of displaying two haptic effects to the user. The first effect is a tactile flow effect up and down along the pen, and the other is a rotation effect about the long axis of the pen. The flow effect is based on the haptic illusion of “apparent tactile motion”, while the rotation effect comes from the reaction torque created by an electric motor placed along the stylus shaft. The stylus is embedded with two vibration actuators at the ends, and a DC motor with a rotating balanced mass in the middle. We show that, it is possible to create flow and rotation effects on the stylus by driving the actuators on the stylus. Furthermore, we show that the timing and the actuation patterns of the vibration actuators and DC motor on the stylus significantly affect the discernibility of the synthesized perceptions; hence these parameters should be selected carefully. Two psychophysical experiments, each performed with 10 subjects, shed light on the discernability of the two haptic effects as a function of various actuation parameters. Our results show that, with carefully selected parameters, the subjects can successfully identify the flow of motion and the direction of rotation with high accuracies.

Haptics has been used as a natural way for humans to communicate with computers in collaborative virtual environments. Human-computer collaboration is typically achieved by sharing control of the task between a human and a computer operator. An important research challenge in the field addresses the need to realize intention recognition and response, which involves a decision making process between the partners. In an earlier study, we implemented a dynamic role exchange mechanism, which realizes decision making by means of trading the parties' control levels on the task. This mechanism proved to show promise of a more intuitive and comfortable communication. Here, we extend our earlier work to further investigate the utility of a role exchange mechanism in dynamic collaboration tasks. An experiment with 30 participants was conducted to compare the utility of a role exchange mechanism with that of a shared control scheme where the human and the computer share control equally at all times. A no guidance condition is considered as a base case to present the benefits of these two guidance schemes more clearly. Our experiment show that the role exchange scheme maximizes the efficiency of the user, which is the ratio of the work done by the user within the task to the energy spent by her. Furthermore, we explored the added benefits of explicitly displaying the control state by embedding visual and vibrotactile sensory cues on top of the role exchange scheme. We observed that such cues decrease performance slightly, probably because they introduce an extra cognitive load, yet they improve the users' sense of collaboration and interaction with the computer. These cues also create a stronger sense of trust for the user towards her partner's control over the task.

Abstract Rigging for facial animation is an important but time‐consuming task, which generally requires experienced artists with knowledge of facial anatomy. In this paper, we investigate whether it is possible to produce a good animatable avatar automatically, given only a 3D static triangle mesh of the head. An automatic mechanism is devised for constructing multi‐layer animatable facial avatars for unseen faces. We evaluate our technique with a variety of models, and give a quantitative analysis of the constructed results. We also designed and conducted a user study for evaluating the perceived quality of the generated expressive animations. The results demonstrate that our method is an appropriate tool for naïve users to customize their personal 3D avatars. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper we present a novel system for driver-vehicle interaction which combines speech recognition with facial-expression recognition to increase intention recognition accuracy in the presence of engine- and road-noise. Our system would allow drivers to interact with in-car devices such as satellite navigation and other telematic or control systems. We describe a pilot study and experiment in which we tested the system, and show that multimodal fusion of speech and facial expression recognition provides higher accuracy than either would do alone.

Current sketch recognition systems treat sketches as images or a collection of strokes, rather than viewing sketching as an interactive and incremental process. We show how viewing sketching as an interactive process allows us to recognize sketches using Hidden Markov Models. We report results of a user study indicating that in certain domains people draw objects using consistent stroke orderings. We show how this consistency, when present, can be used to perform sketch recognition efficiently. This novel approach enables us to have polynomial time algorithms for sketch recognition and segmentation, unlike conventional methods with exponential complexity.