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 …

Deep Learning and its applications have cascaded impactful research and development
with a diverse range of modalities present in the real-world data. More recently, this has …

Recent advances on text-to-image generation have witnessed the rise of diffusion models
which act as powerful generative models. Nevertheless, it is not trivial to exploit such latent …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

Conditional image synthesis aims to create an image according to some multi-modal
guidance in the forms of textual descriptions, reference images, and image blocks to …

Nowadays, scene text recognition has attracted more and more attention due to its various
applications. Most state-of-the-art methods adopt an encoder-decoder framework with …

Both accuracy and efficiency are crucial for image captioning in real-world scenarios.
Although Transformer-based models have gained significant improved captioning …

Over the years, state-of-the-art (SoTA) image captioning methods have achieved promising
results on some evaluation metrics (eg, CIDEr). However, recent findings show that the …

Video captioning (VC) is a fast-moving, cross-disciplinary area of research that comprises
contributions from domains such as computer vision, natural language processing …

Image captioning, also called report generation in medical field, aims to describe visual
content of images in human language, which requires to model semantic relationship …