I am a final-year PhD student working on unsupervised learning under the guidance of Mathieu Aubry (ENPC). During my PhD, I was fortunate to work with Jean Ponce (Inria - WILLOW), Matthew Fisher (Adobe Research) and Alexei Efros (UC Berkeley). Before that, I completed my engineer's degree (=M.Sc.) at Mines Paris.
My research currently focuses on building self-supervised and unsupervised machines to solve visual tasks without manual annotation. Representative papers are highlighted.
We introduce MACARONS, a method that learns in a self-supervised fashion to explore new environments and reconstruct them in 3D using RGB images only.
We build upon unsupervised sprite-based image decomposition approaches to design a generative method to character analysis and recognition in text lines.
A Transformer-based framework to evaluate off-the-shelf features (object-centric and dense representations) for the reasoning task of VQA.
We present UNICORN 🦄, an unsupervised approach leveraging cross-instance consistency for high-quality 3D reconstructions from single-view images.
We characterize 3D shapes as affine transformations of linear families learned without supervision, and showcase its advantages on large shape collections.
An unsupervised learning framework to decompose images into object layers modeled as transformations of learnable sprites.
A simple and interpretable approach to clustering that jointly learns prototypes and their transformations to match data.