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Feedback-guided Data Synthesis for Imbalanced Classification
Reyhane Askari Hemmat, Mohammad Pezeshki, Florian Bordes +2
Current status quo in machine learning is to use static datasets of real images for training, which often come from long-tailed distributions. With the recent advances in generativ…
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity
Melissa Hall, Candace Ross, Adina Williams +3
The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play content creation solutions make it crucial to…
Improved baselines for vision-language pre-training
Enrico Fini, Pietro Astolfi, Adriana Romero-Soriano +2
Contrastive learning has emerged as an efficient framework to learn multimodal representations. CLIP, a seminal work in this area, achieved impressive results by training on paired…
Controllable Image Generation via Collage Representations
Arantxa Casanova, Marlène Careil, Adriana Romero-Soriano +3
Recent advances in conditional generative image models have enabled impressive results. On the one hand, text-based conditional models have achieved remarkable generation quality,…
Instance-Conditioned GAN Data Augmentation for Representation Learning
Pietro Astolfi, Arantxa Casanova, Jakob Verbeek +3
Data augmentation has become a crucial component to train state-of-the-art visual representation models. However, handcrafting combinations of transformations that lead to improved…
Learning to Substitute Ingredients in Recipes
Bahare Fatemi, Quentin Duval, Rohit Girdhar +2
Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid potential allergens, and ease culinary explo…