Rafael S. Rezende — accepted AI-conference papers

2024

Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency

Yannis Kalantidis, Mert Bülent Sarıyıldız, Rafael S. Rezende, Philippe Weinzaepfel, Diane Larlus, Gabriela Csurka

ICLR 2024poster

State-of-the-art visual localization approaches generally rely on a first image retrieval step whose role is crucial. Yet, retrieval often struggles when facing varying conditions, due to e.g. weather or time of day, with dramatic consequences on the visual localization accuracy. In this paper, we i…

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2022

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

Ginger Delmas, Rafael S. Rezende, Gabriela Csurka, Diane Larlus

ICLR 2022poster

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, the latter explicitly calls for new traits, or specifies how some elements of the example image should be changed to retri…

Cited by 111 Source ↗PDF ↗code ↗Scholar ↗

2019

Did It Change? Learning to Detect Point-Of-Interest Changes for Proactive Map Updates

Jerome Revaud, Minhyeok Heo, Rafael S. Rezende, Chanmi You, Seong-Gyun Jeong

CVPR 2019poster

Maps are an increasingly important tool in our daily lives, yet their rich semantic content still largely depends on manual input. Motivated by the broad availability of geo-tagged street-view images, we propose a new task aiming to make the map update process more proactive. We focus on automatical…

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2019

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

Jerome Revaud, Jon Almazan, Rafael S. Rezende, Cesar Roberto de Souza

ICCV 2019poster

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 outperformed traditional methods by leveraging ranking-tailored loss functions, but important theoretical and practical…

Cited by 503 PDF ↗Scholar ↗

2017

Kernel Square-Loss Exemplar Machines for Image Retrieval

Rafael S. Rezende, Joaquin Zepeda, Jean Ponce, Francis Bach, Patrick Perez

CVPR 2017poster

Zepeda and Perez have recently demonstrated the promise of the exemplar SVM (ESVM) as a feature encoder for image retrieval. This paper extends this approach in several directions: We first show that replacing the hinge loss by the square loss in the ESVM cost function significantly reduces encoding…

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2017

SCNet: Learning Semantic Correspondence

Kai Han, Rafael S. Rezende, Bumsub Ham, Kwan-Yee K. Wong, Minsu Cho, Cordelia Schmid +1

ICCV 2017poster

This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category. Previous approaches focus on either combining a spatial regularizer with hand-crafted features, or learning a correspondence model for appearan…

Cited by 159 PDF ↗code ↗Scholar ↗