activity
20172023
most citedSCNet: Learning Semantic Correspondence

24 citations · 38 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV2022★ 8 cited

ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity

Ginger Delmas, Rafael Sampaio de Rezende, Gabriela Csurka +1

An intuitive way to search for images is to use queries composed of an example image and a complementary text. While the first provides rich and implicit context for the search, th…

cs.CV2021

Learning with Label Noise for Image Retrieval by Selecting Interactions

Sarah Ibrahimi, Arnaud Sors, Rafael Sampaio de Rezende +1

Learning with noisy labels is an active research area for image classification. However, the effect of noisy labels on image retrieval has been less studied. In this work, we propo…

cs.CV2021★ 5 cited

Probabilistic Embeddings for Cross-Modal Retrieval

Sanghyuk Chun, Seong Joon Oh, Rafael Sampaio de Rezende +2

Cross-modal retrieval methods build a common representation space for samples from multiple modalities, typically from the vision and the language domains. For images and their cap…

cs.CV2020

StacMR: Scene-Text Aware Cross-Modal Retrieval

Andrés Mafla, Rafael Sampaio de Rezende, Lluís Gómez +2

Recent models for cross-modal retrieval have benefited from an increasingly rich understanding of visual scenes, afforded by scene graphs and object interactions to mention a few.…

cs.CV2019★ 1 cited

Learning with Average Precision: Training Image Retrieval with a Listwise Loss

Jerome Revaud, Jon Almazan, Rafael Sampaio de Rezende +1

Image retrieval can be formulated as a ranking problem where the goal is to order database images by decreasing similarity to the query. Recent deep models for image retrieval have…

cs.CV2017★ 24 cited

SCNet: Learning Semantic Correspondence

Kai Han, Rafael S. Rezende, Bumsub Ham +4

This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category. Previous approaches foc…