Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …

Existing text-to-image diffusion models struggle to synthesize realistic images given dense
captions, where each text prompt provides a detailed description for a specific image region …

Abstract Vision Transformer (ViT) extends the application range of transformers from
language processing to computer vision tasks as being an alternative architecture against …

Abstract Domain generalization (DG) methods aim to achieve generalizability to an unseen
target domain by using only training data from the source domains. Although a variety of DG …

Continual learning is a realistic learning scenario for AI models. Prevalent scenario of
continual learning, however, assumes disjoint sets of classes as tasks and is less realistic …

A good image-to-image translation model should learn a mapping between different visual
domains while satisfying the following properties: 1) diversity of generated images and 2) …

We propose Parallel WaveGAN, a distillation-free, fast, and small-footprint waveform
generation method using a generative adversarial network. In the proposed method, a non …

The problem of class imbalanced data is that the generalization performance of the classifier
deteriorates due to the lack of data from minority classes. In this paper, we propose a novel …

Regional dropout strategies have been proposed to enhance performance of convolutional
neural network classifiers. They have proved to be effective for guiding the model to attend …

Scene text detection methods based on neural networks have emerged recently and have
shown promising results. Previous methods trained with rigid word-level bounding boxes …