Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Connecting Vision and Language plays an essential role in Generative Intelligence. For this
reason, large research efforts have been devoted to image captioning, ie describing images …

We present a conceptually simple, flexible, and general framework for few-shot learning,
where a classifier must learn to recognise new classes given only few examples from each …

Abstract We propose Prototypical Networks for the problem of few-shot classification, where
a classifier must generalize to new classes not seen in the training set, given only a small …

Synthesizing high-quality images from text descriptions is a challenging problem in
computer vision and has many practical applications. Samples generated by existing text-to …

Open-vocabulary models are a promising new paradigm for image classification. Unlike
traditional classification models, open-vocabulary models classify among any arbitrary set of …

Automatic synthesis of realistic images from text would be interesting and useful, but current
AI systems are still far from this goal. However, in recent years generic and powerful …

In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that
allows attention-driven, multi-stage refinement for fine-grained text-to-image generation …

Due to the importance of zero-shot learning, ie, classifying images where there is a lack of
labeled training data, the number of proposed approaches has recently increased steadily …

Although Generative Adversarial Networks (GANs) have shown remarkable success in
various tasks, they still face challenges in generating high quality images. In this paper, we …