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[HTML][HTML] Data augmentation: A comprehensive survey of modern approaches
A Mumuni, F Mumuni - Array, 2022 - Elsevier
To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …
CNN architectures for geometric transformation-invariant feature representation in computer vision: a review
A Mumuni, F Mumuni - SN Computer Science, 2021 - Springer
One of the main challenges in machine vision relates to the problem of obtaining robust
representation of visual features that remain unaffected by geometric transformations. This …
representation of visual features that remain unaffected by geometric transformations. This …
Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)
Employing part-level features offers fine-grained information for pedestrian image
description. A prerequisite of part discovery is that each part should be well located. Instead …
description. A prerequisite of part discovery is that each part should be well located. Instead …
Learning part-based convolutional features for person re-identification
Part-level features offer fine granularity for pedestrian image description. In this article, we
generally aim to learn discriminative part-informed feature for person re-identification. Our …
generally aim to learn discriminative part-informed feature for person re-identification. Our …
Deep CNNs with spatially weighted pooling for fine-grained car recognition
Fine-grained car recognition aims to recognize the category information of a car, such as car
make, car model, or even the year of manufacture. A number of recent studies have shown …
make, car model, or even the year of manufacture. A number of recent studies have shown …
Efficient intrusion detection using multi-player generative adversarial networks (GANs): an ensemble-based deep learning architecture
Intrusion detection systems (IDSs) investigate various attacks, identify malicious patterns,
and implement effective control strategies. With the recent advances in machine learning …
and implement effective control strategies. With the recent advances in machine learning …
Visual explanation by interpretation: Improving visual feedback capabilities of deep neural networks
Interpretation and explanation of deep models is critical towards wide adoption of systems
that rely on them. In this paper, we propose a novel scheme for both interpretation as well as …
that rely on them. In this paper, we propose a novel scheme for both interpretation as well as …
Cross-convolutional-layer pooling for image recognition
Recent studies have shown that a Deep Convolutional Neural Network (DCNN) trained on a
large image dataset can be used as a universal image descriptor and that doing so leads to …
large image dataset can be used as a universal image descriptor and that doing so leads to …
Improving event extraction via multimodal integration
In this paper, we focus on improving Event Extraction (EE) by incorporating visual
knowledge with words and phrases from text documents. We first discover visual patterns …
knowledge with words and phrases from text documents. We first discover visual patterns …
Object discovery from a single unlabeled image by mining frequent itemsets with multi-scale features
The goal of our work is to discover dominant objects in a very general setting where only a
single unlabeled image is given. This is far more challenge than typical co-localization or …
single unlabeled image is given. This is far more challenge than typical co-localization or …