Image retrieval systems aim to find similar images to a query image among an image dataset.
( Image credit: DELF )
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Secondly, it performs hard negative pair mining within the original and synthetic points to select a more informative negative pair for computing the metric learning loss.
The resulting model significantly outperforms state-of-the-art models with similar accuracy in terms of mCE and inference throughput.
Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding.
Computer vision tasks such as image classification, image retrieval and few-shot learning are currently dominated by Euclidean and spherical embeddings, so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances (cosine similarity).
Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually-grounded language understanding skills required for success at these tasks overlap significantly.
Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems.
We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes.