Search Results for author: Mathilde Caron

Found 27 papers, 19 papers with code

Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach

no code implementations31 Oct 2024 Mathilde Caron, Alireza Fathi, Cordelia Schmid, Ahmet Iscen

Web-scale visual entity recognition, the task of associating images with their corresponding entities within vast knowledge bases like Wikipedia, presents significant challenges due to the lack of clean, large-scale training data.

Language Modelling Large Language Model +1

Self-Masking Networks for Unsupervised Adaptation

1 code implementation11 Sep 2024 Alfonso Taboada Warmerdam, Mathilde Caron, Yuki M. Asano

We validate the usefulness of learning binary masks as a fine-tuning method on 8 datasets and 3 model architectures, and we demonstrate the effectiveness of SMNs in 3 label-efficient settings.

A Generative Approach for Wikipedia-Scale Visual Entity Recognition

2 code implementations CVPR 2024 Mathilde Caron, Ahmet Iscen, Alireza Fathi, Cordelia Schmid

In this paper, we address web-scale visual entity recognition, specifically the task of mapping a given query image to one of the 6 million existing entities in Wikipedia.

Guided Diffusion from Self-Supervised Diffusion Features

no code implementations14 Dec 2023 Vincent Tao Hu, Yunlu Chen, Mathilde Caron, Yuki M. Asano, Cees G. M. Snoek, Bjorn Ommer

However, recent studies have revealed that the feature representation derived from diffusion model itself is discriminative for numerous downstream tasks as well, which prompts us to propose a framework to extract guidance from, and specifically for, diffusion models.

Self-Supervised Learning

A Memory Transformer Network for Incremental Learning

no code implementations10 Oct 2022 Ahmet Iscen, Thomas Bird, Mathilde Caron, Alireza Fathi, Cordelia Schmid

We study class-incremental learning, a training setup in which new classes of data are observed over time for the model to learn from.

class-incremental learning Class Incremental Learning +1

Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision

1 code implementation16 Feb 2022 Priya Goyal, Quentin Duval, Isaac Seessel, Mathilde Caron, Ishan Misra, Levent Sagun, Armand Joulin, Piotr Bojanowski

Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images.

 Ranked #1 on Copy Detection on Copydays strong subset (using extra training data)

Action Classification Action Recognition +12

Unsupervised Dense Information Retrieval with Contrastive Learning

6 code implementations16 Dec 2021 Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, Edouard Grave

In this work, we explore the limits of contrastive learning as a way to train unsupervised dense retrievers and show that it leads to strong performance in various retrieval settings.

Contrastive Learning Cross-Lingual Transfer +4

Contrastive Pre-training for Zero-Shot Information Retrieval

no code implementations29 Sep 2021 Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, Edouard Grave

By contrast, in many other NLP tasks, conventional self-supervised pre-training based on masking leads to strong generalization with small number of training examples.

Contrastive Learning Fact Checking +3

XCiT: Cross-Covariance Image Transformers

11 code implementations NeurIPS 2021 Alaaeldin El-Nouby, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob Verbeek, Hervé Jegou

We propose a "transposed" version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries.

Instance Segmentation object-detection +3

Emerging Properties in Self-Supervised Vision Transformers

30 code implementations ICCV 2021 Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin

In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).

Copy Detection Image Retrieval +8

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

18 code implementations NeurIPS 2020 Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin

In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much.

Contrastive Learning Data Augmentation +2

Pruning Convolutional Neural Networks with Self-Supervision

no code implementations10 Jan 2020 Mathilde Caron, Ari Morcos, Piotr Bojanowski, Julien Mairal, Armand Joulin

In this work, we investigate the use of standard pruning methods, developed primarily for supervised learning, for networks trained without labels (i. e. on self-supervised tasks).

Finding Winning Tickets with Limited (or No) Supervision

no code implementations25 Sep 2019 Mathilde Caron, Ari Morcos, Piotr Bojanowski, Julien Mairal, Armand Joulin

The lottery ticket hypothesis argues that neural networks contain sparse subnetworks, which, if appropriately initialized (the winning tickets), are capable of matching the accuracy of the full network when trained in isolation.

Unsupervised Pre-Training of Image Features on Non-Curated Data

2 code implementations ICCV 2019 Mathilde Caron, Piotr Bojanowski, Julien Mairal, Armand Joulin

Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available.

Clustering Self-Supervised Image Classification +1

Deep Clustering for Unsupervised Learning of Visual Features

9 code implementations ECCV 2018 Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze

In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features.

Clustering Deep Clustering +2

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