This paper attacks the challenging problem of video retrieval by text. In such a retrieval
paradigm, an end user searches for unlabeled videos by ad-hoc queries described …

This paper attacks the challenging problem of zero-example video retrieval. In such a
retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described …

This paper presents findings of the eleventh Video Browser Showdown competition, where
sixteen teams competed in known-item and ad-hoc search tasks. Many of the teams utilized …

The rapid growth of user-generated videos on the Internet has intensified the need for text-
based video retrieval systems. Traditional methods mainly favor the concept-based …

Ad-hoc video search (AVS) is an important yet challenging problem in multimedia retrieval.
Different from previous concept-based methods, we propose a fully deep learning method …

Abstract The Video Browser Showdown addresses difficult video search challenges through
an annual interactive evaluation campaign attracting research teams focusing on interactive …

Research in the Vision and Language area encompasses challenging topics that seek to
connect visual and textual information. When the visual information is related to videos, this …

In this paper we revisit feature fusion, an old-fashioned topic, in the new context of text-to-
video retrieval. Different from previous research that considers feature fusion only at one …

Retrieving unlabeled videos by textual queries, known as Ad-hoc Video Search (AVS), is a
core theme in multimedia data management and retrieval. The success of AVS counts on …

In this study we compare three different fine-tuning strategies in order to investigate the best
way to transfer the parameters of popular deep convolutional neural networks that were …