Search Results for author:
Found 10 papers, 5 papers with code
ViDaS Video Depth-aware Saliency Network
We introduce ViDaS, a two-stream, fully convolutional Video, Depth-Aware Saliency network to address the problem of attention modeling ``in-the-wild", via saliency prediction in videos.
ChildBot: Multi-Robot Perception and Interaction with Children
In this paper we present an integrated robotic system capable of participating in and performing a wide range of educational and entertainment tasks, in collaboration with one or more children.
How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking
We present a novel multi-attentional convolutional architecture to tackle the problem of real-time RGB-D 6D object pose tracking of single, known objects.
STAViS: Spatio-Temporal AudioVisual Saliency Network
We introduce STAViS, a spatio-temporal audiovisual saliency network that combines spatio-temporal visual and auditory information in order to efficiently address the problem of saliency estimation in videos.
Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection
Detecting visual relationships, i. e. <Subject, Predicate, Object> triplets, is a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch.
Fusing Body Posture with Facial Expressions for Joint Recognition of Affect in Child-Robot Interaction
In this paper we address the problem of multi-cue affect recognition in challenging scenarios such as child-robot interaction.
SUSiNet: See, Understand and Summarize it
In this work we propose a multi-task spatio-temporal network, called SUSiNet, that can jointly tackle the spatio-temporal problems of saliency estimation, action recognition and video summarization.
Ranked #66 on
Action Recognition
on HMDB-51
(using extra training data)
A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention task
In this work we tackle the problem of child engagement estimation while children freely interact with a robot in their room.
LSTM-based Network for Human Gait Stability Prediction in an Intelligent Robotic Rollator
In this work, we present a novel framework for on-line human gait stability prediction of the elderly users of an intelligent robotic rollator using Long Short Term Memory (LSTM) networks, fusing multimodal RGB-D and Laser Range Finder (LRF) data from non-wearable sensors.
Multimodal Visual Concept Learning with Weakly Supervised Techniques
Despite the availability of a huge amount of video data accompanied by descriptive texts, it is not always easy to exploit the information contained in natural language in order to automatically recognize video concepts.
