12 citations · 23 across the 9 of their papers we have counts for
6 papers · 1 filter
CLIPRerank: An Extremely Simple Method for Improving Ad-hoc Video Search
Aozhu Chen, Fangming Zhou, Ziyuan Wang +1
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 h…
ChinaOpen: A Dataset for Open-world Multimodal Learning
Aozhu Chen, Ziyuan Wang, Chengbo Dong +5
This paper introduces ChinaOpen, a dataset sourced from Bilibili, a popular Chinese video-sharing website, for open-world multimodal learning. While the state-of-the-art multimodal…
Learn to Understand Negation in Video Retrieval
Ziyue Wang, Aozhu Chen, Fan Hu +1
Negation is a common linguistic skill that allows human to express what we do NOT want. Naturally, one might expect video retrieval to support natural-language queries with negatio…
Towards Making a Trojan-horse Attack on Text-to-Image Retrieval
Fan Hu, Aozhu Chen, Xirong Li
While deep learning based image retrieval is reported to be vulnerable to adversarial attacks, existing works are mainly on image-to-image retrieval with their attacks performed at…
Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval
Fan Hu, Aozhu Chen, Ziyue Wang +3
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…
What Matters for Ad-hoc Video Search? A Large-scale Evaluation on TRECVID
Aozhu Chen, Fan Hu, Zihan Wang +2
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 te…