MultiMAE: Multi-modal Multi-task Masked Autoencoders

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Publications associées (31)

CLIP the Gap: A Single Domain Generalization Approach for Object Detection

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Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remai ...

Ieee Computer Soc2023

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

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Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

EPFL2023

Improving Generalization of Pretrained Language Models

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In this dissertation, we propose multiple methods to improve transfer learning for pretrained language models (PLMs). Broadly, transfer learning is a powerful technique in natural language processing, where a language model is first pre-trained on a data-r ...

EPFL2023

Deep Learning Generalization with Limited and Noisy Labels

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Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

4M: Massively Multimodal Masked Modeling

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Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

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Supervised learning and inference of spiking neural networks with temporal coding

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The way biological brains carry out advanced yet extremely energy efficient signal processing remains both fascinating and unintelligible. It is known however that at least some areas of the brain perform fast and low-cost processing relying only on a smal ...

EPFL2023

Estimating and Improving the Robustness of Attributions in Text

Ádám Dániel Ivánkay

End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...

EPFL2023

Toward contactless human thermal monitoring: A framework for Machine Learning-based human thermo-physiology modeling augmented with computer vision

Alexandre Massoud Alahi, Dolaana Khovalyg, Mohamad Rida, Mohamed Ossama Ahmed Abdelfattah

The transition towards a human-centered indoor climate is beneficial from occupants’ thermal comfort and from an energy reduction perspective. However, achieving this goal requires the knowledge of the thermal state of individuals at the level of body part ...

2023

Timelapse: Rhone mouth in Geneva Lake - 2017

Valentin Kindschi

The dataset is a collection of RGB and thermal images of the Rhone mouth in Geneva Lake, taken every minute in 2017. ...

EPFL Infoscience2023

Timelapse: Geneva Lake, in front of Chexbres - 2019

Valentin Kindschi

The dataset is a collection of RGB and thermal images of the Rhone mouth in Geneva Lake, taken every minute in 2019. ...

EPFL Infoscience2023

Timelapse: Rhone mouth in Geneva Lake - 2019

Valentin Kindschi

The dataset is a collection of RGB and thermal images of the Rhone mouth in Geneva Lake, taken every minute in 2019. ...

EPFL Infoscience2023

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