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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 …
A survey of synthetic data augmentation methods in machine vision
A Mumuni, F Mumuni, NK Gerrar - Machine Intelligence Research, 2024 - Springer
The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …
neural network (CNN) models using large-scale image datasets that are representative of …
[PDF][PDF] Multimodal image synthesis and editing: A survey
As information exists in various modalities in real world, effective interaction and fusion
among multimodal information plays a key role for the creation and perception of multimodal …
among multimodal information plays a key role for the creation and perception of multimodal …
Turning a clip model into a scene text detector
The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown
great potential in various downstream tasks via leveraging the pretrained vision and …
great potential in various downstream tasks via leveraging the pretrained vision and …
Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …
target domain, but it requires to access the source data which often raises concerns in data …
Auto-regressive image synthesis with integrated quantization
Deep generative models have achieved conspicuous progress in realistic image synthesis
with multifarious conditional inputs, while generating diverse yet high-fidelity images …
with multifarious conditional inputs, while generating diverse yet high-fidelity images …
Unbalanced feature transport for exemplar-based image translation
Despite the great success of GANs in images translation with different conditioned inputs
such as semantic segmentation and edge map, generating high-fidelity images with …
such as semantic segmentation and edge map, generating high-fidelity images with …
Diverse image inpainting with bidirectional and autoregressive transformers
Image inpainting is an underdetermined inverse problem, which naturally allows diverse
contents to fill up the missing or corrupted regions realistically. Prevalent approaches using …
contents to fill up the missing or corrupted regions realistically. Prevalent approaches using …
High-resolution image inpainting using multi-scale neural patch synthesis
Recent advances in deep learning have shown exciting promise in filling large holes in
natural images with semantically plausible and context aware details, impacting …
natural images with semantically plausible and context aware details, impacting …
Mask textspotter v3: Segmentation proposal network for robust scene text spotting
Recent end-to-end trainable methods for scene text spotting, integrating detection and
recognition, showed much progress. However, most of the current arbitrary-shape scene text …
recognition, showed much progress. However, most of the current arbitrary-shape scene text …