Portrait de Florian Bordes n'est pas disponible

Florian Bordes

Alumni

Publications

An Introduction to Vision-Language Modeling
Florian Bordes
Richard Yuanzhe Pang
Anurag Ajay
Alexander C. Li
Adrien Bardes
Suzanne Petryk
Oscar Mañas
Zhiqiu Lin
Anas Mahmoud
Bargav Jayaraman
Mark Ibrahim
Melissa Hall
Yunyang Xiong
Jonathan Lebensold
Candace Ross
Srihari Jayakumar
Chuan Guo
Diane Bouchacourt
Haider Al-Tahan
Karthik Padthe … (voir 21 de plus)
Vasu Sharma
Huijuan Xu 0001
Xiaoqing Ellen Tan
Megan Richards
Samuel Lavoie
Pietro Astolfi
Reyhane Askari Hemmat
Jun Chen
Kushal Tirumala
Rim Assouel
Mazda Moayeri
Arjang Talattof
Kamalika Chaudhuri
Zechun Liu
Xilun Chen
Quentin Garrido
Karen Ullrich
Aishwarya Agrawal
Kate Saenko
Asli Celikyilmaz
Vikas Chandra
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From h… (voir plus)aving a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.
2024-05-27
ArXiv (prépublication)
doi.org
arxiv.org
An Introduction to Vision-Language Modeling
Florian Bordes
Richard Yuanzhe Pang
Anurag Ajay
Alexander C. Li
Adrien Bardes
Suzanne Petryk
Oscar Mañas
Zhiqiu Lin
Anas Mahmoud
Bargav Jayaraman
Mark Ibrahim
Melissa Hall
Yunyang Xiong
Jonathan Lebensold
Candace Ross
Srihari Jayakumar
Chuan Guo
Diane Bouchacourt
Haider Al-Tahan
Karthik Padthe … (voir 21 de plus)
Vasu Sharma
Huijuan Xu 0001
Xiaoqing Ellen Tan
Megan Richards
Samuel Lavoie
Pietro Astolfi
Reyhane Askari Hemmat
Jun Chen
Kushal Tirumala
Rim Assouel
Mazda Moayeri
Arjang Talattof
Kamalika Chaudhuri
Zechun Liu
Xilun Chen
Quentin Garrido
Karen Ullrich
Aishwarya Agrawal
Kate Saenko
Asli Celikyilmaz
Vikas Chandra
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From h… (voir plus)aving a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.
2024-05-27
ArXiv (prépublication)
doi.org
arxiv.org
An Introduction to Vision-Language Modeling
Florian Bordes
Richard Yuanzhe Pang
Anurag Ajay
Alexander C. Li
Adrien Bardes
Suzanne Petryk
Oscar Mañas
Zhiqiu Lin
Anas Mahmoud
Bargav Jayaraman
Mark Ibrahim
Melissa Hall
Yunyang Xiong
Jonathan Lebensold
Candace Ross
Srihari Jayakumar
Chuan Guo
Diane Bouchacourt
Haider Al-Tahan
Karthik Padthe … (voir 21 de plus)
Vasu Sharma
Huijuan Xu 0001
Xiaoqing Ellen Tan
Megan Richards
Samuel Lavoie
Pietro Astolfi
Reyhane Askari Hemmat
Jun Chen
Kushal Tirumala
Rim Assouel
Mazda Moayeri
Arjang Talattof
Kamalika Chaudhuri
Zechun Liu
Xilun Chen
Quentin Garrido
Karen Ullrich
Aishwarya Agrawal
Kate Saenko
Asli Celikyilmaz
Vikas Chandra
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From h… (voir plus)aving a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.
2024-05-27
ArXiv (prépublication)
doi.org
arxiv.org
An Introduction to Vision-Language Modeling
Florian Bordes
Richard Yuanzhe Pang
Anurag Ajay
Alexander C. Li
Adrien Bardes
Suzanne Petryk
Oscar Mañas
Zhiqiu Lin
Anas Mahmoud
Bargav Jayaraman
Mark Ibrahim
Melissa Hall
Yunyang Xiong
Jonathan Lebensold
Candace Ross
Srihari Jayakumar
Chuan Guo
Diane Bouchacourt
Haider Al-Tahan
Karthik Padthe … (voir 21 de plus)
Vasu Sharma
Huijuan Xu 0001
Xiaoqing Ellen Tan
Megan Richards
Samuel Lavoie
Pietro Astolfi
Reyhane Askari Hemmat
Jun Chen
Kushal Tirumala
Rim Assouel
Mazda Moayeri
Arjang Talattof
Kamalika Chaudhuri
Zechun Liu
Xilun Chen
Quentin Garrido
Karen Ullrich
Aishwarya Agrawal
Kate Saenko
Asli Celikyilmaz
Vikas Chandra
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From h… (voir plus)aving a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.
2024-05-27
ArXiv (prépublication)
doi.org
arxiv.org
An Introduction to Vision-Language Modeling
Florian Bordes
Richard Yuanzhe Pang
Anurag Ajay
Alexander C. Li
Adrien Bardes
Suzanne Petryk
Oscar Mañas
Zhiqiu Lin
Anas Mahmoud
Bargav Jayaraman
Mark Ibrahim
Melissa Hall
Yunyang Xiong
Jonathan Lebensold
Candace Ross
Srihari Jayakumar
Chuan Guo
Diane Bouchacourt
Haider Al-Tahan
Karthik Padthe … (voir 21 de plus)
Vasu Sharma
Huijuan Xu 0001
Xiaoqing Ellen Tan
Megan Richards
Samuel Lavoie
Pietro Astolfi
Reyhane Askari Hemmat
Jun Chen
Kushal Tirumala
Rim Assouel
Mazda Moayeri
Arjang Talattof
Kamalika Chaudhuri
Zechun Liu
Xilun Chen
Quentin Garrido
Karen Ullrich
Aishwarya Agrawal
Kate Saenko
Asli Celikyilmaz
Vikas Chandra
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From h… (voir plus)aving a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with technology. However, there are many challenges that need to be addressed to improve the reliability of those models. While language is discrete, vision evolves in a much higher dimensional space in which concepts cannot always be easily discretized. To better understand the mechanics behind mapping vision to language, we present this introduction to VLMs which we hope will help anyone who would like to enter the field. First, we introduce what VLMs are, how they work, and how to train them. Then, we present and discuss approaches to evaluate VLMs. Although this work primarily focuses on mapping images to language, we also discuss extending VLMs to videos.
2024-05-27
ArXiv (prépublication)
doi.org
arxiv.org
An Introduction to Vision-Language Modeling
Florian Bordes
Richard Yuanzhe Pang
Anurag Ajay
Alexander C. Li
Adrien Bardes
Suzanne Petryk
Oscar Mañas
Zhiqiu Lin
Anas Mahmoud
Bargav Jayaraman
Mark Ibrahim
Melissa Hall
Yunyang Xiong
Jonathan Lebensold
Candace Ross
Srihari Jayakumar
Chuan Guo
Diane Bouchacourt
Haider Al-Tahan
Karthik Padthe … (voir 21 de plus)
Vasu Sharma
Huijuan Xu 0001
Xiaoqing Ellen Tan
Megan Richards
Samuel Lavoie
Pietro Astolfi
Reyhane Askari Hemmat
Jun Chen
Kushal Tirumala
Rim Assouel
Mazda Moayeri
Arjang Talattof
Kamalika Chaudhuri
Zechun Liu
Xilun Chen
Quentin Garrido
Karen Ullrich
Aishwarya Agrawal
Kate Saenko
Asli Celikyilmaz
Vikas Chandra
2024-05-27
ArXiv (prépublication)
doi.org
arxiv.org
Stochastic positional embeddings improve masked image modeling
Amir Bar
Florian Bordes
Assaf Shocher
Mahmoud Assran
Pascal Vincent
Nicolas Ballas
Trevor Darrell
Amir Globerson
Yann Lecun
Masked Image Modeling (MIM) is a promising self-supervised learning approach that enables learning from unlabeled images. Despite its recent… (voir plus) success, learning good representations through MIM remains challenging because it requires predicting the right semantic content in accurate locations. For example, given an incomplete picture of a dog, we can guess that there is a tail, but we cannot determine its exact location. In this work, we propose to incorporate location uncertainty into MIM by using stochastic positional embeddings (StoP). Specifically, we condition the model on stochastic masked token positions drawn from a Gaussian distribution. StoP reduces overfitting to location features and guides the model toward learning features that are more robust to location uncertainties. Quantitatively, StoP improves downstream MIM performance on a variety of downstream tasks, including
2024-05-01
ICML.cc/2024/Conference (poster)
openreview.net
openreview.net
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Jack Urbanek
Florian Bordes
Pietro Astolfi
Mary Williamson
Vasu Sharma
Adriana Romero Soriano
Curation methods for massive vision-language datasets trade off between dataset size and quality. However, even the highest quality of avail… (voir plus)able curated captions are far too short to capture the rich visual detail in an image. To show the value of dense and highly-aligned image-text pairs, we collect the Densely Captioned Images (DCI) dataset, containing 7805 natural images human-annotated with mask-aligned descriptions averaging above 1000 words each. With precise and reliable captions associated with specific parts of an image, we can evaluate vision-language models' (VLMs) understanding of image content with a novel task that matches each caption with its corresponding subcrop. As current models are often limited to 77 text tokens, we also introduce a summarized version (sDCI) in which each caption length is limited. We show that modern techniques that make progress on standard benchmarks do not correspond with significant improvement on our sDCI based benchmark. Lastly, we finetune CLIP using sDCI and show significant improvements over the baseline despite a small training set. By releasing the first human annotated dense image captioning dataset, we hope to enable the development of new benchmarks or finetuning recipes for the next generation of VLMs to come.
2023-12-14
ArXiv (prépublication)
doi.org
arxiv.org
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Jack Urbanek
Florian Bordes
Pietro Astolfi
Mary Williamson
Vasu Sharma
Adriana Romero Soriano
Curation methods for massive vision-language datasets trade off between dataset size and quality. However, even the highest quality of avail… (voir plus)able curated captions are far too short to capture the rich visual detail in an image. To show the value of dense and highly-aligned image-text pairs, we collect the Densely Captioned Images (DCI) dataset, containing 7805 natural images human-annotated with mask-aligned descriptions averaging above 1000 words each. With precise and reliable captions associated with specific parts of an image, we can evaluate vision-language models' (VLMs) understanding of image content with a novel task that matches each caption with its corresponding subcrop. As current models are often limited to 77 text tokens, we also introduce a summarized version (sDCI) in which each caption length is limited. We show that modern techniques that make progress on standard benchmarks do not correspond with significant improvement on our sDCI based benchmark. Lastly, we finetune CLIP using sDCI and show significant improvements over the baseline despite a small training set. By releasing the first human annotated dense image captioning dataset, we hope to enable the development of new benchmarks or finetuning recipes for the next generation of VLMs to come.
2023-12-14
ArXiv (prépublication)
doi.org
arxiv.org
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Jack Urbanek
Florian Bordes
Pietro Astolfi
Mary Williamson
Vasu Sharma
Adriana Romero Soriano
Curation methods for massive vision-language datasets trade off between dataset size and quality. However, even the highest quality of avail… (voir plus)able curated captions are far too short to capture the rich visual detail in an image. To show the value of dense and highly-aligned image-text pairs, we collect the Densely Captioned Images (DCI) dataset, containing 7805 natural images human-annotated with mask-aligned descriptions averaging above 1000 words each. With precise and reliable captions associated with specific parts of an image, we can evaluate vision-language models' (VLMs) understanding of image content with a novel task that matches each caption with its corresponding subcrop. As current models are often limited to 77 text tokens, we also introduce a summarized version (sDCI) in which each caption length is limited. We show that modern techniques that make progress on standard benchmarks do not correspond with significant improvement on our sDCI based benchmark. Lastly, we finetune CLIP using sDCI and show significant improvements over the baseline despite a small training set. By releasing the first human annotated dense image captioning dataset, we hope to enable the development of new benchmarks or finetuning recipes for the next generation of VLMs to come.
2023-12-14
ArXiv (prépublication)
doi.org
arxiv.org
Feedback-guided Data Synthesis for Imbalanced Classification
Reyhane Askari Hemmat
Mohammad Pezeshki
Florian Bordes
Michal Drozdzal
Adriana Romero Soriano
Current status quo in machine learning is to use static datasets of real images for training, which often come from long-tailed distribution… (voir plus)s. With the recent advances in generative models, researchers have started augmenting these static datasets with synthetic data, reporting moderate performance improvements on classification tasks. We hypothesize that these performance gains are limited by the lack of feedback from the classifier to the generative model, which would promote the usefulness of the generated samples to improve the classifier's performance. In this work, we introduce a framework for augmenting static datasets with useful synthetic samples, which leverages one-shot feedback from the classifier to drive the sampling of the generative model. In order for the framework to be effective, we find that the samples must be close to the support of the real data of the task at hand, and be sufficiently diverse. We validate three feedback criteria on a long-tailed dataset (ImageNet-LT) as well as a group-imbalanced dataset (NICO++). On ImageNet-LT, we achieve state-of-the-art results, with over 4 percent improvement on underrepresented classes while being twice efficient in terms of the number of generated synthetic samples. NICO++ also enjoys marked boosts of over 5 percent in worst group accuracy. With these results, our framework paves the path towards effectively leveraging state-of-the-art text-to-image models as data sources that can be queried to improve downstream applications.
2023-10-30
NeurIPS.cc/2023/Workshop/SyntheticData4ML (poster)
doi.org
openreview.net
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
Florian Bordes
Shashank Shekhar
Mark Ibrahim
Diane Bouchacourt
Pascal Vincent
Ari S. Morcos
Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render a… (voir plus)s many data samples as needed, (ii) precisely control each scene and yield granular ground truth labels (and captions), (iii) precisely control distribution shifts between training and testing to isolate variables of interest for sound experimentation. Despite such promise, the use of synthetic image data is still limited -- and often played down -- mainly due to their lack of realism. Most works therefore rely on datasets of real images, which have often been scraped from public images on the internet, and may have issues with regards to privacy, bias, and copyright, while offering little control over how objects precisely appear. In this work, we present a path to democratize the use of photorealistic synthetic data: we develop a new generation of interactive environments for representation learning research, that offer both controllability and realism. We use the Unreal Engine, a powerful game engine well known in the entertainment industry, to produce PUG (Photorealistic Unreal Graphics) environments and datasets for representation learning. In this paper, we demonstrate the potential of PUG to enable more rigorous evaluations of vision models.
openreview.net