This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

Understanding human behavior and activity facilitates advancement of numerous real-world
applications, and is critical for video analysis. Despite the progress of action recognition …

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 …

Most modern approaches in temporal action localization divide this problem into two parts:(i)
short-term feature extraction and (ii) long-range temporal boundary localization. Due to the …

Detecting actions in untrimmed videos is an important yet challenging task. In this paper, we
present the structured segment network (SSN), a novel framework which models the …

This research strives for natural language moment retrieval in long, untrimmed video
streams. The problem is not trivial especially when a video contains multiple moments of …

Weakly-supervised temporal action localization is a problem of learning an action
localization model with only video-level action labeling available. The general framework …

Weakly-supervised temporal action localization aims to localize actions in untrimmed videos
with only video-level labels. Most existing methods address this problem with a" localization …

The task of action detection aims at deducing both the action category and localization of the
start and end moment for each action instance in a long, untrimmed video. While vision …

To effectively comprehend human actions, we have developed a Deep Neural Network
(DNN) that utilizes inner spatiotemporal non-locality to capture meaningful semantic context …