The recent wave of AI-generated content (AIGC) has witnessed substantial success in
computer vision, with the diffusion model playing a crucial role in this achievement. Due to …

Human activity recognition (HAR) is a very active yet challenging and demanding area of
computer science. Due to the articulated nature of human motion, it is not trivial to detect …

This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables
learning powerful and transferable video-text representations for multimodal understanding …

We study joint video and language (VL) pre-training to enable cross-modality learning and
benefit plentiful downstream VL tasks. Existing works either extract low-quality video …

Learning text-video embeddings usually requires a dataset of video clips with manually
provided captions. However, such datasets are expensive and time consuming to create and …

We present a new large-scale multilingual video description dataset, VATEX, which contains
over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions …

The goal of multimodal summarization is to extract the most important information from
different modalities to form summaries. Unlike unimodal summarization, the multimodal …

Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video
models still rely on manually annotated data. With the recent introduction of the HowTo100M …

One of the factors that have hindered progress in the areas of sign language recognition,
translation, and production is the absence of large annotated datasets. Towards this end, we …

The evaluation campaign of the 19th International Conference on Spoken Language
Translation featured eight shared tasks:(i) Simultaneous speech translation,(ii) Offline …