17 citations · 55 across the 11 of their papers we have counts for
11 papers
From Prompt to Production:Automating Brand-Safe Marketing Imagery with Text-to-Image Models
Parmida Atighehchian, Henry Wang, Andrei Kapustin +4
Text-to-image models have made significant strides, producing impressive results in generating images from textual descriptions. However, creating a scalable pipeline for deploying…
Domain Adaptation of VLM for Soccer Video Understanding
Tiancheng Jiang, Henry Wang, Md Sirajus Salekin +2
Vision Language Models (VLMs) have demonstrated strong performance in multi-modal tasks by effectively aligning visual and textual representations. However, most video understandin…
Mitigating stereotypical biases in text to image generative systems
Piero Esposito, Parmida Atighehchian, Anastasis Germanidis +1
State-of-the-art generative text-to-image models are known to exhibit social biases and over-represent certain groups like people of perceived lighter skin tones and men in their o…
Structure and Content-Guided Video Synthesis with Diffusion Models
Patrick Esser, Johnathan Chiu, Parmida Atighehchian +2
Text-guided generative diffusion models unlock powerful image creation and editing tools. While these have been extended to video generation, current approaches that edit the conte…
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Gaurav Sahu, Pau Rodriguez, Issam H. Laradji +3
Data augmentation is a widely employed technique to alleviate the problem of data scarcity. In this work, we propose a prompting-based approach to generate labelled training data f…
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian +3
We examine a simple stochastic strategy for adapting well-known single-point acquisition functions to allow batch active learning. Unlike acquiring the top-K points from the pool s…
Can Active Learning Preemptively Mitigate Fairness Issues?
Frédéric Branchaud-Charron, Parmida Atighehchian, Pau Rodríguez +2
Dataset bias is one of the prevailing causes of unfairness in machine learning. Addressing fairness at the data collection and dataset preparation stages therefore becomes an essen…
Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron +7
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms. Enabling the design of datasets to te…
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images
Issam Laradji, Pau Rodriguez, Frederic Branchaud-Charron +5
One of the key challenges in the battle against the Coronavirus (COVID-19) pandemic is to detect and quantify the severity of the disease in a timely manner. Computed tomographies…
Bayesian active learning for production, a systematic study and a reusable library
Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste
Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific inputs. While there are many papers on new active…
DECoVaC: Design of Experiments with Controlled Variability Components
Thomas Boquet, Laure Delisle, Denis Kochetkov +4
Reproducible research in Machine Learning has seen a salutary abundance of progress lately: workflows, transparency, and statistical analysis of validation and test performance. We…