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 …

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

Video clip retrieval and captioning tasks play an essential role in multimodal research and
are the fundamental research problem for multimodal understanding and generation. The …

Video captioning is a challenging task since it requires generating sentences describing
various diverse and complex videos. Existing video captioning models lack adequate visual …

Large-scale pre-trained multi-modal models (eg, CLIP) demonstrate strong zero-shot
transfer capability in many discriminative tasks. Their adaptation to zero-shot image …

Video description refers to understanding visual content and transforming that acquired
understanding into automatic textual narration. It bridges the key AI fields of computer vision …

With the development of machine learning techniques, the attention of research has been
moved from single-modal learning to multi-modal learning, as real-world data exist in the …

We introduce a zero-shot video captioning method that employs two frozen networks: the
GPT-2 language model and the CLIP image-text matching model. The matching score is …

Video captioning is an automated collection of natural language phrases that explains the
contents in video frames. Because of the incomparable performance of deep learning in the …

Video captioning combines video understanding and language generation. Different from
image captioning that describes a static image with details of almost every object, video …