Search Results for author: Alexandre Bernardino

Found 17 papers, 4 papers with code

Attention on Classification for Fire Segmentation

no code implementations4 Nov 2021 Milad Niknejad, Alexandre Bernardino

The network is jointly trained for both segmentation and classification, leading to improvement in the performance of the single-task image segmentation methods, and the previous methods proposed for fire segmentation.

Classification Semantic Segmentation

Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots

no code implementations9 Feb 2021 Alexandre Almeida, Pedro Vicente, Alexandre Bernardino

We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy.

Hand Segmentation

Online Body Schema Adaptation through Cost-Sensitive Active Learning

no code implementations26 Jan 2021 Gonçalo Cunha, Pedro Vicente, Alexandre Bernardino, Ricardo Ribeiro, Plínio Moreno

The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.

Active Learning

Designing Personalized Interaction of a Socially Assistive Robot for Stroke Rehabilitation Therapy

no code implementations13 Jul 2020 Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia

The research of a socially assistive robot has a potential to augment and assist physical therapy sessions for patients with neurological and musculoskeletal problems (e. g. stroke).

Attention Filtering for Multi-person Spatiotemporal Action Detection on Deep Two-Stream CNN Architectures

no code implementations21 Jul 2019 João Antunes, Pedro Abreu, Alexandre Bernardino, Asim Smailagic, Daniel Siewiorek

Our method, using fovea attention filtering and our generalized binary loss, achieves a relative video mAP improvement of 20% over the two-stream baseline in AVA, and is competitive with the state-of-the-art in the UCF101-24.

Action Detection General Classification +1

Weighted Multisource Tradaboost

no code implementations26 Mar 2019 João Antunes, Alexandre Bernardino, Asim Smailagic, Daniel Siewiorek

In this paper we propose an improved method for transfer learning that takes into account the balance between target and source data.

Transfer Learning

Beyond the Self: Using Grounded Affordances to Interpret and Describe Others' Actions

1 code implementation26 Feb 2019 Giovanni Saponaro, Lorenzo Jamone, Alexandre Bernardino, Giampiero Salvi

It then uses this information to learn a mapping between its own actions and those performed by a human in a shared environment.

Action Recognition

Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots

no code implementations9 Apr 2018 Giovanni Saponaro, Pedro Vicente, Atabak Dehban, Lorenzo Jamone, Alexandre Bernardino, José Santos-Victor

One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i. e., the ability to perceive object affordances.

Decision Making

Language Bootstrapping: Learning Word Meanings From Perception-Action Association

1 code implementation27 Nov 2017 Giampiero Salvi, Luis Montesano, Alexandre Bernardino, José Santos-Victor

The model is based on an affordance network, i. e., a mapping between robot actions, robot perceptions, and the perceived effects of these actions upon objects.

Language Acquisition speech-recognition +1

Interactive Robot Learning of Gestures, Language and Affordances

no code implementations24 Nov 2017 Giovanni Saponaro, Lorenzo Jamone, Alexandre Bernardino, Giampiero Salvi

A growing field in robotics and Artificial Intelligence (AI) research is human-robot collaboration, whose target is to enable effective teamwork between humans and robots.

Unscented Bayesian Optimization for Safe Robot Grasping

no code implementations7 Mar 2016 José Nogueira, Ruben Martinez-Cantin, Alexandre Bernardino, Lorenzo Jamone

We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space.

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