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 …

For quantifying progress in Ad-hoc Video Search (AVS), the annual TRECVID AVS task is an
important international evaluation. Solutions submitted by the task participants vary in terms …

Cross-modal representation learning has become a new normal for bridging the semantic
gap between text and visual data. Learning modality agnostic representations in a …

We summarize our TRECVID 2023 Ad-hoc Video Search (AVS) experiments. We focus on
leveraging pre-trained multimodal models for video and text representation. For video …

Video Corpus Visual Answer Localization (VCVAL) includes question-related video retrieval
and visual answer localization in the videos. Specifically, we use text-to-text retrieval to find …

In this study, we propose a novel approach for Ad-hoc Video Search that leverages the
power of image search engines to synthesize query images for corresponding textual …

We summarize our TRECVID 2022 Ad-hoc Video Search (AVS) experiments. Our solution is
built with two new techniques, namely Lightweight Attentional Feature Fusion (LAFF) for …

Ad-hoc Video Search (AVS) enables users to search for unlabeled video content using on-
the-fly textual queries. Current deep learning-based models for AVS are trained to optimize …

This year, we explore generation-augmented retrieval for the TRECVid AVS task.
Specifically, the understanding of textual query is enhanced by three generations, including …

Cross-modal representation learning has become a new normal for bridging the semantic
gap between text and visual data. Learning modality agnostic representations in a …