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Publications

Publications by Ricardo Pereira Cruz

2023

Interpretability-Guided Human Feedback During Neural Network Training

Authors
Serrano e Silva, P; Cruz, R; Shihavuddin, ASM; Gonçalves, T;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract

2023

Unimodal Distributions for Ordinal Regression

Authors
Cardoso, JS; Cruz, R; Albuquerque, T;

Publication
CoRR

Abstract

2022

Deep learning-based system for real-time behavior recognition and closed-loop control of behavioral mazes using depth sensing

Authors
Geros, AF; Cruz, R; de Chaumont, F; Cardoso, JS; Aguiar, P;

Publication

Abstract
Robust quantification of animal behavior is fundamental in experimental neuroscience research. Systems providing automated behavioral assessment are an important alternative to manual measurements avoiding problems such as human bias, low reproducibility and high cost. Integrating these tools with closed-loop control systems creates conditions to correlate environment and behavioral expressions effectively, and ultimately explain the neural foundations of behavior. We present an integrated solution for automated behavioral analysis of rodents using deep learning networks on video streams acquired from a depth-sensing camera. The use of depth sensors has notable advantages: tracking/classification performance is improved and independent of animals' coat color, and videos can be recorded in dark conditions without affecting animals' natural behavior. Convolutional and recurrent layers were combined in deep network architectures, and both spatial and temporal representations were successfully learned for a 4-classes behavior classification task (standstill, walking, rearing and grooming). Integration with Arduino microcontrollers creates an easy-to-use control platform providing low-latency feedback signals based on the deep learning automatic classification of animal behavior. The complete system, combining depth-sensor camera, computer, and Arduino microcontroller, allows simple mapping of input-output control signals using the animal's current behavior and position. For example, a feeder can be controlled not by pressing a lever but by the animal behavior itself. An integrated graphical user interface completes a user-friendly and cost-effective solution for animal tracking and behavior classification. This open-software/open-hardware platform can boost the development of customized protocols for automated behavioral research, and support ever more sophisticated, reliable and reproducible behavioral neuroscience experiments.

2023

Evaluating the Performance of Explanation Methods on Ordinal Regression CNN Models

Authors
Barbero-Gómez, J; Cruz, R; Cardoso, JS; Gutiérrez, PA; Hervás-Martínez, C;

Publication
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2023, PT II

Abstract
This paper introduces an evaluation procedure to validate the efficacy of explanation methods for Convolutional Neural Network (CNN) models in ordinal regression tasks. Two ordinal methods are contrasted against a baseline using cross-entropy, across four datasets. A statistical analysis demonstrates that attribution methods, such as Grad-CAM and IBA, perform significantly better when used with ordinal regression CNN models compared to a baseline approach in most ordinal and nominal metrics. The study suggests that incorporating ordinal information into the attribution map construction process may improve the explanations further.

2024

Active Supervision: Human in the Loop

Authors
Cruz, RPM; Shihavuddin, ASM; Maruf, MH; Cardoso, JS;

Publication
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I

Abstract
After the learning process, certain types of images may not be modeled correctly because they were not well represented in the training set. These failures can then be compensated for by collecting more images from the real-world and incorporating them into the learning process - an expensive process known as active learning. The proposed twist, called active supervision, uses the model itself to change the existing images in the direction where the boundary is less defined and requests feedback from the user on how the new image should be labeled. Experiments in the context of class imbalance show the technique is able to increase model performance in rare classes. Active human supervision helps provide crucial information to the model during training that the training set lacks.

2024

YOLOMM - You Only Look Once for Multi-modal Multi-tasking

Authors
Campos, F; Cerqueira, FG; Cruz, RPM; Cardoso, JS;

Publication
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I

Abstract
Autonomous driving can reduce the number of road accidents due to human error and result in safer roads. One important part of the system is the perception unit, which provides information about the environment surrounding the car. Currently, most manufacturers are using not only RGB cameras, which are passive sensors that capture light already in the environment but also Lidar. This sensor actively emits laser pulses to a surface or object and measures reflection and time-of-flight. Previous work, YOLOP, already proposed a model for object detection and semantic segmentation, but only using RGB. This work extends it for Lidar and evaluates performance on KITTI, a public autonomous driving dataset. The implementation shows improved precision across all objects of different sizes. The implementation is entirely made available: https://github.com/filipepcampos/yolomm.

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