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 has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning (MMDL) is to create models …

Transformer-based architectures represent the state of the art in sequence modeling tasks
like machine translation and language understanding. Their applicability to multi-modal …

Recent progress on visual question answering has explored the merits of grid features for
vision language tasks. Meanwhile, transformer-based models have shown remarkable …

Recent progress on fine-grained visual recognition and visual question answering has
featured Bilinear Pooling, which effectively models the 2nd order interactions across multi …

Attention mechanisms are widely used in current encoder/decoder frameworks of image
captioning, where a weighted average on encoded vectors is generated at each time step to …

Attention mechanisms are now widely used in image captioning models. However, most
attention models only focus on visual features. When generating syntax related words, little …

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 …

Tremendous progress has been made in recent years in developing better image captioning
models, yet most of them rely on a separate object detector to extract regional features …

Image captioning aims to automatically generate a natural language description of a given
image, and most state-of-the-art models have adopted an encoder-decoder framework. The …