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Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remai ...
This report presents a study on the development and application of a Region-based Convolutional Neural Network, Faster RCNN and a more complex one, TransVOD, to locate solar coronal jets using data from the Solar Dynamic Observatory (SDO). The study focus ...
Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor ...
Los Alamitos2023
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The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between syntheti ...
Los Alamitos2023
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Recent years have witnessed significant advance- ment in face recognition (FR) techniques, with their applications widely spread in people’s lives and security-sensitive areas. There is a growing need for reliable interpretations of decisions of such syste ...
2024
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Recently, it has been exposed that some modern facial recognition systems could discriminate specific demographic groups and may lead to unfair attention with respect to various facial attributes such as gender and origin. The main reason are the biases in ...
IEEE2023
Object detection plays a critical role in various computer vision applications, encompassingdomains like autonomous vehicles, object tracking, and scene understanding. These applica-tions rely on detectors that generate bounding boxes around known object c ...
EPFL2023
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The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...
In the past years, deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in both verification and identification scenarios. Despite the high accuracy, they are often criticized for lacking explainability. The ...
2023
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Despite the huge success of deep convolutional neural networks in face recognition (FR) tasks, current methods lack explainability for their predictions because of their ``black-box'' nature. In recent years, studies have been carried out to give an interp ...
