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20142026
most citedHard Negative Mixing for Contrastive Learning

269 citations · 550 across the 34 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

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.CV2020

Concept Generalization in Visual Representation Learning

Mert Bulent Sariyildiz, Yannis Kalantidis, Diane Larlus +1

Measuring concept generalization, i.e., the extent to which models trained on a set of (seen) visual concepts can be leveraged to recognize a new set of (unseen) concepts, is a pop…

cs.CV2020

Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning

Riccardo Volpi, Diane Larlus, Grégory Rogez

Most standard learning approaches lead to fragile models which are prone to drift when sequentially trained on samples of a different nature - the well-known "catastrophic forgetti…

cs.CV2020★ 269 cited

Hard Negative Mixing for Contrastive Learning

Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion +2

Contrastive learning has become a key component of self-supervised learning approaches for computer vision. By learning to embed two augmented versions of the same image close to e…

cs.CV2020

Learning Visual Representations with Caption Annotations

Mert Bulent Sariyildiz, Julien Perez, Diane Larlus

Pretraining general-purpose visual features has become a crucial part of tackling many computer vision tasks. While one can learn such features on the extensively-annotated ImageNe…