489 citations · 493 across the 8 of their papers we have counts for
9 papers
Improved Baselines for Data-efficient Perceptual Augmentation of LLMs
Théophane Vallaeys, Mustafa Shukor, Matthieu Cord +1
The abilities of large language models (LLMs) have recently progressed to unprecedented levels, paving the way to novel applications in a wide variety of areas. In computer vision,…
Better (pseudo-)labels for semi-supervised instance segmentation
François Porcher, Camille Couprie, Marc Szafraniec +1
Despite the availability of large datasets for tasks like image classification and image-text alignment, labeled data for more complex recognition tasks, such as detection and segm…
Unlocking Pre-trained Image Backbones for Semantic Image Synthesis
Tariq Berrada, Jakob Verbeek, Camille Couprie +1
Semantic image synthesis, i.e., generating images from user-provided semantic label maps, is an important conditional image generation task as it allows to control both the content…
Multi-Domain Learning with Modulation Adapters
Ekaterina Iakovleva, Karteek Alahari, Jakob Verbeek
Deep convolutional networks are ubiquitous in computer vision, due to their excellent performance across different tasks for various domains. Models are, however, often trained in…
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,…
Are Visual Recognition Models Robust to Image Compression?
João Maria Janeiro, Stanislav Frolov, Alaaeldin El-Nouby +1
Reducing the data footprint of visual content via image compression is essential to reduce storage requirements, but also to reduce the bandwidth and latency requirements for trans…