Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

With the urgent demand for generalized deep models, many pre-trained big models are
proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT) …

Recent text-to-image generation models like DreamBooth have made remarkable progress
in generating highly customized images of a target subject, by fine-tuning an``expert …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Talking face generation, also known as speech-to-lip generation, reconstructs facial motions
concerning lips given coherent speech input. The previous studies revealed the importance …

Understanding verbs is crucial to modelling how people and objects interact with each other
and the environment through space and time. Recently, state-of-the-art video-language …

Conditioned and composed image retrieval extend CBIR systems by combining a query
image with an additional text that expresses the intent of the user, describing additional …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

The canonical approach to video action recognition dictates a neural network model to do a
classic and standard 1-of-N majority vote task. They are trained to predict a fixed set of …