Search Results for author: Pau Rodriguez

Found 33 papers, 23 papers with code

StarVector: Generating Scalable Vector Graphics Code from Images

no code implementations17 Dec 2023 Juan A. Rodriguez, Shubham Agarwal, Issam H. Laradji, Pau Rodriguez, David Vazquez, Christopher Pal, Marco Pedersoli

These visual tokens are pre-pended to the SVG token embeddings, and the sequence is modeled by the StarCoder model using next-token prediction, effectively learning to align the visual and code tokens.

Code Generation Vector Graphics

Continual Learning of Diffusion Models with Generative Distillation

1 code implementation23 Nov 2023 Sergi Masip, Pau Rodriguez, Tinne Tuytelaars, Gido M. van de Ven

We demonstrate that our approach significantly improves the continual learning performance of generative replay with only a moderate increase in the computational costs.

Continual Learning Denoising +1

GEO-Bench: Toward Foundation Models for Earth Monitoring

1 code implementation NeurIPS 2023 Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan David Sherwin, Hannah Kerner, Björn Lütjens, Jeremy Andrew Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vazquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiao Xiang Zhu

Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.

FigGen: Text to Scientific Figure Generation

1 code implementation1 Jun 2023 Juan A Rodriguez, David Vazquez, Issam Laradji, Marco Pedersoli, Pau Rodriguez

The generative modeling landscape has experienced tremendous growth in recent years, particularly in generating natural images and art.

Language Decision Transformers with Exponential Tilt for Interactive Text Environments

no code implementations10 Feb 2023 Nicolas Gontier, Pau Rodriguez, Issam Laradji, David Vazquez, Christopher Pal

Text-based game environments are challenging because agents must deal with long sequences of text, execute compositional actions using text and learn from sparse rewards.

Offline RL

OCR-VQGAN: Taming Text-within-Image Generation

2 code implementations19 Oct 2022 Juan A. Rodriguez, David Vazquez, Issam Laradji, Marco Pedersoli, Pau Rodriguez

To alleviate this problem, we present OCR-VQGAN, an image encoder, and decoder that leverages OCR pre-trained features to optimize a text perceptual loss, encouraging the architecture to preserve high-fidelity text and diagram structure.

Image Generation Optical Character Recognition (OCR)

Constraining Representations Yields Models That Know What They Don't Know

no code implementations30 Aug 2022 Joao Monteiro, Pau Rodriguez, Pierre-Andre Noel, Issam Laradji, David Vazquez

In the add-on case, the original neural network's inference head is completely unaffected (so its accuracy remains the same) but we now have the option to use TAC's own confidence and prediction when determining which course of action to take in an hypothetical production workflow.

Workflow Discovery from Dialogues in the Low Data Regime

1 code implementation24 May 2022 Amine El Hattami, Stefania Raimondo, Issam Laradji, David Vazquez, Pau Rodriguez, Chris Pal

We propose and evaluate an approach that conditions models on the set of possible actions, and we show that using this strategy, we can improve WD performance.

Workflow Discovery

Overcoming challenges in leveraging GANs for few-shot data augmentation

1 code implementation30 Mar 2022 Christopher Beckham, Issam Laradji, Pau Rodriguez, David Vazquez, Derek Nowrouzezahrai, Christopher Pal

In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance.

Classification Data Augmentation +1

Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark

no code implementations1 Dec 2021 Alexandre Lacoste, Evan David Sherwin, Hannah Kerner, Hamed Alemohammad, Björn Lütjens, Jeremy Irvin, David Dao, Alex Chang, Mehmet Gunturkun, Alexandre Drouin, Pau Rodriguez, David Vazquez

Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.

Continual Learning via Local Module Composition

1 code implementation NeurIPS 2021 Oleksiy Ostapenko, Pau Rodriguez, Massimo Caccia, Laurent Charlin

We introduce local module composition (LMC), an approach to modular CL where each module is provided a local structural component that estimates a module's relevance to the input.

Continual Learning Transfer Learning

A Survey of Self-Supervised and Few-Shot Object Detection

1 code implementation27 Oct 2021 Gabriel Huang, Issam Laradji, David Vazquez, Simon Lacoste-Julien, Pau Rodriguez

Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image.

Few-Shot Object Detection Instance Segmentation +3

Overcoming Label Ambiguity with Multi-label Iterated Learning

no code implementations29 Sep 2021 Sai Rajeswar Mudumba, Pau Rodriguez, Soumye Singhal, David Vazquez, Aaron Courville

This ambiguity biases models towards a single prediction, which could result in the suppression of classes that tend to co-occur in the data.

Multi-Label Learning Transfer Learning

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

2 code implementations ICCV 2021 Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam Laradji, Laurent Charlin, David Vazquez

Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems.

Attribute BIG-bench Machine Learning +2

Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations

no code implementations1 Jan 2021 Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam H. Laradji, Laurent Charlin, David Vazquez

In computer vision applications, most methods explain models by displaying the regions in the input image that they focus on for their prediction, but it is difficult to improve models based on these explanations since they do not indicate why the model fail.

Attribute counterfactual +1

Hierarchical Residual Attention Network for Single Image Super-Resolution

1 code implementation8 Dec 2020 Parichehr Behjati, Pau Rodriguez, Armin Mehri, Isabelle Hupont, Carles Fernández Tena, Jordi Gonzalez

In order to make an efficient use of the residual features, these are hierarchically aggregated into feature banks for posterior usage at the network output.

Image Super-Resolution

Affinity LCFCN: Learning to Segment Fish with Weak Supervision

1 code implementation6 Nov 2020 Issam Laradji, Alzayat Saleh, Pau Rodriguez, Derek Nowrouzezahrai, Mostafa Rahimi Azghadi, David Vazquez

Leading automatic approaches rely on fully-supervised segmentation models to acquire these measurements but these require collecting per-pixel labels -- also time consuming and laborious: i. e., it can take up to two minutes per fish to generate accurate segmentation labels, almost always requiring at least some manual intervention.

Segmentation

CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions

1 code implementation14 Sep 2020 Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez, Massimo Caccia, Qi She, Yu Chen, Quentin Jodelet, Ruiping Wang, Zheda Mai, David Vazquez, German I. Parisi, Nikhil Churamani, Marc Pickett, Issam Laradji, Davide Maltoni

In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous.

Benchmarking Continual Learning

OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network

1 code implementation5 Aug 2020 Parichehr Behjati, Pau Rodriguez, Armin Mehri, Isabelle Hupont, Jordi Gonzalez, Carles Fernandez Tena

Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs).

Super-Resolution

A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images

3 code implementations4 Jul 2020 Issam Laradji, Pau Rodriguez, Oscar Mañas, Keegan Lensink, Marco Law, Lironne Kurzman, William Parker, David Vazquez, Derek Nowrouzezahrai

Thus, we propose a consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images.

LOOC: Localize Overlapping Objects with Count Supervision

1 code implementation3 Jul 2020 Issam H. Laradji, Rafael Pardinas, Pau Rodriguez, David Vazquez

For localization, LOOC achieves a strong new baseline in the novel problem setup where only count supervision is available.

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