1 code implementation • ICCV 2023 • Ioana Croitoru, Simion-Vlad Bogolin, Samuel Albanie, Yang Liu, Zhaowen Wang, Seunghyun Yoon, Franck Dernoncourt, Hailin Jin, Trung Bui
To study this problem, we propose the first dataset of untrimmed, long-form tutorial videos for the task of Moment Detection called the Behance Moment Detection (BMD) dataset.
1 code implementation • CVPR 2022 • Simion-Vlad Bogolin, Ioana Croitoru, Hailin Jin, Yang Liu, Samuel Albanie
In this work we first show that, despite their effectiveness, state-of-the-art joint embeddings suffer significantly from the longstanding "hubness problem" in which a small number of gallery embeddings form the nearest neighbours of many queries.
Ranked #5 on Video Retrieval on QuerYD
1 code implementation • ICCV 2021 • Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu, Hailin Jin, Andrew Zisserman, Samuel Albanie, Yang Liu
In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerful video encoders.
no code implementations • COLING 2020 • Simion-Vlad Bogolin, Ioana Croitoru, Marius Leordeanu
Automatically describing videos in natural language is an ambitious problem, which could bridge our understanding of vision and language.
no code implementations • 14 Aug 2018 • Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu
We train a student deep network to predict the output of a teacher pathway that performs unsupervised object discovery in videos or large image collections.
no code implementations • 5 Jun 2018 • Iulia Duta, Andrei Liviu Nicolicioiu, Simion-Vlad Bogolin, Marius Leordeanu
Here we propose an approach to describe videos in natural language by reaching a consensus among multiple encoder-decoder networks.
no code implementations • ICCV 2017 • Ioana Croitoru, Simion-Vlad Bogolin, Marius Leordeanu
Our approach is different from the published literature that performs unsupervised discovery in videos or in collections of images at test time.