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Found 6 papers, 3 papers with code
Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations
In this work, we address the problem of universal image embedding, where a single universal model is trained and used in multiple domains.
Global Features are All You Need for Image Retrieval and Reranking
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)
Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information
We showcase its application to the landmark recognition domain, presenting a detailed analysis and the final fairer landmark rankings.
Towards A Fairer Landmark Recognition Dataset
To create a more comprehensive and equitable dataset, we start by defining the fair relevance of a landmark to the world population.
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
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)
Unifying Deep Local and Global Features for Image Search
Image retrieval is the problem of searching an image database for items that are similar to a query image.
