We are creating multimedia contents everyday and everywhere. While automatic content
generation has played a fundamental challenge to multimedia community for decades …

In this paper, we propose a novel approach to video captioning based on adversarial
learning and long short-term memory (LSTM). With this solution concept, we aim at …

Generative adversarial models (GANs) continue to produce advances in terms of the visual
quality of still images, as well as the learning of temporal correlations. However, few works …

While significant progress has been made in the image captioning task, video description is
still in its infancy due to the complex nature of video data. Generating multi-sentence …

In this paper, we propose a generative model, Temporal Generative Adversarial Nets
(TGAN), which can learn a semantic representation of unlabeled videos, and is capable of …

The quality of the data and annotation upper-bounds the quality of a downstream model.
While there exist large text corpora and image-text pairs high-quality video-text data is much …

In this paper, we focus on the problem of applying the transformer structure to video
captioning effectively. The vanilla transformer is proposed for uni-modal language …

Recent video and language pretraining frameworks lack the ability to generate sentences.
We present Multimodal Video Generative Pretraining (MV-GPT), a new pretraining …

Current metrics for video captioning are mostly based on the text-level comparison between
reference and candidate captions. However, they have some insuperable drawbacks, eg …

Dense video captioning aims to identify the events of interest in an input video, and generate
descriptive captions for each event. Previous approaches usually follow a two-stage …