10,369 research outputs found
Smart School Multimodal Dataset and Challenges
As part of a research project aiming to explore the notion of ‘smart school’ (especially for STEM education) in Estonia, we are developing classrooms and schools that incorporate data gathering not only from digital traces, but also physical ones (through a variety of sensors). This workshop contribution describes briefly the setting and our initial efforts in setting up a classroom that is able to generate such a multimodal dataset. The paper also describes some of the most important challenges that we are facing as we setup the project and attempt to build up such dataset, focusing on the specifics of doing it in an everyday, authentic school setting. We believe these challenges provide a nice sample of those that the multimodal learning analytics (MMLA) community will have to face as it transitions from an emergent to a mainstream community of research and practice
Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot
We explore new aspects of assistive living on smart human-robot interaction
(HRI) that involve automatic recognition and online validation of speech and
gestures in a natural interface, providing social features for HRI. We
introduce a whole framework and resources of a real-life scenario for elderly
subjects supported by an assistive bathing robot, addressing health and hygiene
care issues. We contribute a new dataset and a suite of tools used for data
acquisition and a state-of-the-art pipeline for multimodal learning within the
framework of the I-Support bathing robot, with emphasis on audio and RGB-D
visual streams. We consider privacy issues by evaluating the depth visual
stream along with the RGB, using Kinect sensors. The audio-gestural recognition
task on this new dataset yields up to 84.5%, while the online validation of the
I-Support system on elderly users accomplishes up to 84% when the two
modalities are fused together. The results are promising enough to support
further research in the area of multimodal recognition for assistive social
HRI, considering the difficulties of the specific task. Upon acceptance of the
paper part of the data will be publicly available
Video Storytelling: Textual Summaries for Events
Bridging vision and natural language is a longstanding goal in computer
vision and multimedia research. While earlier works focus on generating a
single-sentence description for visual content, recent works have studied
paragraph generation. In this work, we introduce the problem of video
storytelling, which aims at generating coherent and succinct stories for long
videos. Video storytelling introduces new challenges, mainly due to the
diversity of the story and the length and complexity of the video. We propose
novel methods to address the challenges. First, we propose a context-aware
framework for multimodal embedding learning, where we design a Residual
Bidirectional Recurrent Neural Network to leverage contextual information from
past and future. Second, we propose a Narrator model to discover the underlying
storyline. The Narrator is formulated as a reinforcement learning agent which
is trained by directly optimizing the textual metric of the generated story. We
evaluate our method on the Video Story dataset, a new dataset that we have
collected to enable the study. We compare our method with multiple
state-of-the-art baselines, and show that our method achieves better
performance, in terms of quantitative measures and user study.Comment: Published in IEEE Transactions on Multimedi
Understanding face and eye visibility in front-facing cameras of smartphones used in the wild
Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations
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