Search Results for author: Jaime S. Cardoso

Found 17 papers, 7 papers with code

Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes

no code implementations25 May 2022 Wilson Silva, Maria Carvalho, Carlos Mavioso, Maria J. Cardoso, Jaime S. Cardoso

Currently, there is no gold standard for evaluating the aesthetic outcome of breast cancer treatment.

A survey on attention mechanisms for medical applications: are we moving towards better algorithms?

1 code implementation26 Apr 2022 Tiago Gonçalves, Isabel Rio-Torto, Luís F. Teixeira, Jaime S. Cardoso

This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.

Image Classification Medical Image Classification +1

FocusFace: Multi-task Contrastive Learning for Masked Face Recognition

1 code implementation28 Oct 2021 Pedro C. Neto, Fadi Boutros, João Ribeiro Pinto, Naser Damer, Ana F. Sequeira, Jaime S. Cardoso

The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks.

Contrastive Learning Face Recognition +1

My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition

no code implementations2 Aug 2021 Pedro C. Neto, Fadi Boutros, João Ribeiro Pinto, Mohsen Saffari, Naser Damer, Ana F. Sequeira, Jaime S. Cardoso

The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS).

Face Recognition

Tackling unsupervised multi-source domain adaptation with optimism and consistency

1 code implementation29 Sep 2020 Diogo Pernes, Jaime S. Cardoso

It has been known for a while that the problem of multi-source domain adaptation can be regarded as a single source domain adaptation task where the source domain corresponds to a mixture of the original source domains.

Domain Adaptation

SpaMHMM: Sparse Mixture of Hidden Markov Models for Graph Connected Entities

1 code implementation31 Mar 2019 Diogo Pernes, Jaime S. Cardoso

We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology.

Dimensional emotion recognition using visual and textual cues

no code implementations3 May 2018 Pedro M. Ferreira, Diogo Pernes, Kelwin Fernandes, Ana Rebelo, Jaime S. Cardoso

This paper addresses the problem of automatic emotion recognition in the scope of the One-Minute Gradual-Emotional Behavior challenge (OMG-Emotion challenge).

Emotion Recognition

Deep Local Binary Patterns

no code implementations17 Nov 2017 Kelwin Fernandes, Jaime S. Cardoso

Local Binary Pattern (LBP) is a traditional descriptor for texture analysis that gained attention in the last decade.

Texture Classification Translation

802.11 Wireless Simulation and Anomaly Detection using HMM and UBM

1 code implementation10 Jul 2017 Anisa Allahdadi, Ricardo Morla, Jaime S. Cardoso

Despite the growing popularity of 802. 11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior among other reasons.

Networking and Internet Architecture

oAdaBoost: An AdaBoost Variant for Ordinal Classification

1 code implementation ICPRAM 2015 Joao Costa, Jaime S. Cardoso

Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval.

Classification General Classification +2

Active Mining of Parallel Video Streams

no code implementations14 May 2014 Samaneh Khoshrou, Jaime S. Cardoso, Luis F. Teixeira

The practicality of a video surveillance system is adversely limited by the amount of queries that can be placed on human resources and their vigilance in response.

Incremental Learning

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