24 citations · 38 across the 7 of their papers we have counts for
8 papers
AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation
Jheng-Hong Yang, Carlos Lassance, Rafael Sampaio de Rezende +4
This paper presents the AToMiC (Authoring Tools for Multimedia Content) dataset, designed to advance research in image/text cross-modal retrieval. While vision-language pretrained…
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…
Simple and Effective Balance of Contrastive Losses
Arnaud Sors, Rafael Sampaio de Rezende, Sarah Ibrahimi +1
Contrastive losses have long been a key ingredient of deep metric learning and are now becoming more popular due to the success of self-supervised learning. Recent research has sho…
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…
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…
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.…
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…
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…