241 citations · 615 across the 38 of their papers we have counts for
40 papers
PoM: Efficient Image and Video Generation with the Polynomial Mixer
David Picard, Nicolas Dufour
Diffusion models based on Multi-Head Attention (MHA) have become ubiquitous to generate high quality images and videos. However, encoding an image or a video as a sequence of patch…
Don't drop your samples! Coherence-aware training benefits Conditional diffusion
Nicolas Dufour, Victor Besnier, Vicky Kalogeiton +1
Conditional diffusion models are powerful generative models that can leverage various types of conditional information, such as class labels, segmentation masks, or text captions.…
Analysis of Classifier-Free Guidance Weight Schedulers
Xi Wang, Nicolas Dufour, Nefeli Andreou +4
Classifier-Free Guidance (CFG) enhances the quality and condition adherence of text-to-image diffusion models. It operates by combining the conditional and unconditional prediction…
An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning
Grégoire Petit, Michael Soumm, Eva Feillet +4
Class-Incremental Learning (CIL) aims to build classification models from data streams. At each step of the CIL process, new classes must be integrated into the model. Due to catas…
LRVS-Fashion: Extending Visual Search with Referring Instructions
Simon Lepage, Jérémie Mary, David Picard
This paper introduces a new challenge for image similarity search in the context of fashion, addressing the inherent ambiguity in this domain stemming from complex images. We prese…
Alphazzle: Jigsaw Puzzle Solver with Deep Monte-Carlo Tree Search
Marie-Morgane Paumard, Hedi Tabia, David Picard
Solving jigsaw puzzles requires to grasp the visual features of a sequence of patches and to explore efficiently a solution space that grows exponentially with the sequence length.…