Search Results for author:
Found 14 papers, 2 papers with code
A deep Natural Language Inference predictor without language-specific training data
The model has been evaluated over machine translated Stanford NLI test dataset, machine translated Multi-Genre NLI test dataset, and manually translated RTE3-ITA test dataset.
Markerless human pose estimation for biomedical applications: a survey
Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application.
I-SPLIT: Deep Network Interpretability for Split Computing
A neuron is important if its gradient with respect to the correct class decision is high.
Pose Forecasting in Industrial Human-Robot Collaboration
Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting.
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging.
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments
Our POMP method uses as input the current pose of an agent (e. g. a robot) and a RGB-D frame.
ESAD: Endoscopic Surgeon Action Detection Dataset
In this work, we take aim towards increasing the effectiveness of surgical assistant robots.
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD).
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
In this work, we explore the correlation between people trajectories and their head orientations.
MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses
Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures.
“Seeing is Believing”: Pedestrian Trajectory Forecasting Using Visual Frustum of Attention
In this paper we show the importance of the head pose estimation in the task of trajectory forecasting.
Indirect Match Highlights Detection with Deep Convolutional Neural Networks
Highlights in a sport video are usually referred as actions that stimulate excitement or attract attention of the audience.
The S-Hock Dataset: Analyzing Crowds at the Stadium
In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc.
F-formation Detection: Individuating Free-standing Conversational Groups in Images
Then, as a main contribution, we present a brand new method for the automatic detection of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular we are able to codify in a computational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people.
