Publications associées à Stable Diffusion

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Alexandre Massoud Alahi, Mohamed Ossama Ahmed Abdelfattah, Mariam Ahmed Mahmoud Hegazy Hassan

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

2024

Learnable latent embeddings for joint behavioural and neural analysis

Mackenzie Mathis, Steffen Schneider, Jin Hwa Lee

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural re ...

NATURE PORTFOLIO2023

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

Sébastien Marcel, Parsa Rahimi Noshanagh, Christophe René Joseph Ecabert

Recently, it has been exposed that some modern facial recognition systems could discriminate specific demographic groups and may lead to unfair attention with respect to various facial attributes such as gender and origin. The main reason are the biases in ...

IEEE2023

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert

Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

BMVA2023

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Pierre Dillenbourg, Richard Lee Davis, Kevin Gonyop Kim, Thiemo Wambsganss, Wei Jiang

This paper investigates the potential impact of deep generative models on the work of creative professionals, specifically focusing on fashion design. We argue that current generative modeling tools lack critical features that would make them useful creati ...

Association for Computing Machinery2023

Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages

Karl Aberer, Rémi Philippe Lebret, Negar Foroutan Eghlidi

Vision-Language Pre-training (VLP) has advanced the performance of many visionlanguage tasks, such as image-text retrieval, visual entailment, and visual reasoning. The pre-training mostly utilizes lexical databases and image queries in English. Previous w ...

Assoc Computational Linguistics-Acl2023

Towards learning-based denoising of light fields

Touradj Ebrahimi, Michela Testolina, Tomás Soares De Carvalho Feith

In recent years, new emerging immersive imaging modalities, e.g. light fields, have been receiving growing attention, becoming increasingly widespread over the years. Light fields are often captured through multi-camera arrays or plenoptic cameras, with th ...

2023

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Devis Tuia, Marc Conrad Russwurm, Lloyd Haydn Hughes, Oleg Antropov, Sudipan Saha

In this study, we examine the potential of several self-supervised deep learning models in predicting forest attributes and detecting forest changes using ESA Sentinel-1 and Sentinel-2 images. The performance of the proposed deep learning models is compare ...

The Institute of Electrical and Electronics Engineers, Inc2023

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Stable Diffusion

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Learnable latent embeddings for joint behavioural and neural analysis

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Text Representation Learning for Low Cost Natural Language Understanding

Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages

Towards learning-based denoising of light fields

Dense Image-based Predictions for Comics Analysis

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Graph Chatbot

Chattez avec Graph Search

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Learnable latent embeddings for joint behavioural and neural analysis

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

Text Representation Learning for Low Cost Natural Language Understanding

Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages

Towards learning-based denoising of light fields

Dense Image-based Predictions for Comics Analysis