no code implementations • ICCV 2023 • Nikolaos-Antonios Ypsilantis, KaiFeng Chen, Bingyi Cao, Mário Lipovský, Pelin Dogan-Schönberger, Grzegorz Makosa, Boris Bluntschli, Mojtaba Seyedhosseini, Ondřej Chum, André Araujo
In this work, we address the problem of universal image embedding, where a single universal model is trained and used in multiple domains.
2 code implementations • ICCV 2023 • Shihao Shao, KaiFeng Chen, Arjun Karpur, Qinghua Cui, Andre Araujo, Bingyi Cao
Image retrieval systems conventionally use a two-stage paradigm, leveraging global features for initial retrieval and local features for reranking.
Ranked #1 on Image Retrieval on RParis (Hard)
no code implementations • 2 Jun 2022 • Zu Kim, André Araujo, Bingyi Cao, Cam Askew, Jack Sim, Mike Green, N'Mah Fodiatu Yilla, Tobias Weyand
We showcase its application to the landmark recognition domain, presenting a detailed analysis and the final fairer landmark rankings.
no code implementations • 19 Aug 2021 • Zu Kim, André Araujo, Bingyi Cao, Cam Askew, Jack Sim, Mike Green, N'Mah Fodiatu Yilla, Tobias Weyand
To create a more comprehensive and equitable dataset, we start by defining the fair relevance of a landmark to the world population.
4 code implementations • 3 Apr 2020 • Tobias Weyand, Andre Araujo, Bingyi Cao, Jack Sim
GLDv2 is the largest such dataset to date by a large margin, including over 5M images and 200k distinct instance labels.
Ranked #1 on Landmark Recognition on Google Landmarks Dataset v2 (recognition, validation) (using extra training data)
4 code implementations • ECCV 2020 • Bingyi Cao, Andre Araujo, Jack Sim
Image retrieval is the problem of searching an image database for items that are similar to a query image.