141 citations · 380 across the 16 of their papers we have counts for
19 papers
Uplift Modeling: from Causal Inference to Personalization
Felipe Moraes, Hugo Manuel Proença, Anastasiia Kornilova +2
Uplift modeling is a collection of machine learning techniques for estimating causal effects of a treatment at the individual or subgroup levels. Over the last years, causality and…
Incremental Profit per Conversion: a Response Transformation for Uplift Modeling in E-Commerce Promotions
Hugo Manuel Proença, Felipe Moraes
Promotions play a crucial role in e-commerce platforms, and various cost structures are employed to drive user engagement. This paper focuses on promotions with response-dependent…
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses
Joana C. Costa, Tiago Roxo, Hugo Proença +1
Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing. However, Deep Neural Networks (DNNs) are v…
WASD: A Wilder Active Speaker Detection Dataset
Tiago Roxo, Joana C. Costa, Pedro R. M. Inácio +1
Current Active Speaker Detection (ASD) models achieve great results on AVA-ActiveSpeaker (AVA), using only sound and facial features. Although this approach is applicable in movie…
Information Retrieval: Recent Advances and Beyond
Kailash A. Hambarde, Hugo Proenca
In this paper, we provide a detailed overview of the models used for information retrieval in the first and second stages of the typical processing chain. We discuss the current st…
YinYang-Net: Complementing Face and Body Information for Wild Gender Recognition
Tiago Roxo, Hugo Proença
Soft biometrics inference in surveillance scenarios is a topic of interest for various applications, particularly in security-related areas. However, soft biometric analysis is not…
Is Gender "In-the-Wild" Inference Really a Solved Problem?
Tiago Roxo, Hugo Proença
Soft biometrics analysis is seen as an important research topic, given its relevance to various applications. However, even though it is frequently seen as a solved task, it can st…
The UU-Net: Reversible Face De-Identification for Visual Surveillance Video Footage
Hugo Proença
We propose a reversible face de-identification method for low resolution video data, where landmark-based techniques cannot be reliably used. Our solution is able to generate a pho…
A Symbolic Temporal Pooling method for Video-based Person Re-Identification
S V Aruna Kumar, Ehsan Yaghoubi, Hugo Proença
In video-based person re-identification, both the spatial and temporal features are known to provide orthogonal cues to effective representations. Such representations are currentl…
The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices
S. V. Aruna Kumar, Ehsan Yaghoubi, Abhijit Das +2
Over the last decades, the world has been witnessing growing threats to the security in urban spaces, which has augmented the relevance given to visual surveillance solutions able…
An Attention-Based Deep Learning Model for Multiple Pedestrian Attributes Recognition
Ehsan Yaghoubi, Diana Borza, João Neves +2
The automatic characterization of pedestrians in surveillance footage is a tough challenge, particularly when the data is extremely diverse with cluttered backgrounds, and subjects…
A Quadruplet Loss for Enforcing Semantically Coherent Embeddings in Multi-output Classification Problems
Hugo Proença, Ehsan Yaghoubi, Pendar Alirezazadeh
This paper describes one objective function for learning semantically coherent feature embeddings in multi-output classification problems, i.e., when the response variables have di…
Unconstrained Periocular Recognition: Using Generative Deep Learning Frameworks for Attribute Normalization
Luiz A. Zanlorensi, Hugo Proença, David Menotti
Ocular biometric systems working in unconstrained environments usually face the problem of small within-class compactness caused by the multiple factors that jointly degrade the qu…
Person Re-identification: Implicitly Defining the Receptive Fields of Deep Learning Classification Frameworks
Ehsan Yaghoubi, Diana Borza, Aruna Kumar +1
The \emph{receptive fields} of deep learning classification models determine the regions of the input data that have the most significance for providing correct decisions. The prim…
Deep Representations for Cross-spectral Ocular Biometrics
Luiz A. Zanlorensi, Diego R. Lucio, Alceu S. Britto +2
One of the major challenges in ocular biometrics is the cross-spectral scenario, i.e., how to match images acquired in different wavelengths (typically visible (VIS) against near-i…
Forensic shoe-print identification: a brief survey
Imad Rida, Lunke Fei, Hugo Proença +2
As an advanced research topic in forensics science, automatic shoe-print identification has been extensively studied in the last two decades, since shoe marks are the clues most fr…
Adaptive diffusion constrained total variation scheme with application to `cartoon + texture + edge' image decomposition
Juan C. Moreno, V. B. Surya Prasath, D. Vorotnikov +2
We consider an image decomposition model involving a variational (minimization) problem and an evolutionary partial differential equation (PDE). We utilize a linear inhomogenuous d…
Robust Periocular Recognition By Fusing Sparse Representations of Color and Geometry Information
Juan C. Moreno, V. B. S. Prasath, Gil Santos +1
In this paper, we propose a re-weighted elastic net (REN) model for biometric recognition. The new model is applied to data separated into geometric and color spatial components. T…
Brain MRI Segmentation with Fast and Globally Convex Multiphase Active Contours
Juan C. Moreno, V. B. S. Prasath, Hugo Proenca +1
Multiphase active contour based models are useful in identifying multiple regions with different characteristics such as the mean values of regions. This is relevant in brain magne…