The preprint of our work (with Salah Zaiem and Robin Algayres) on sample dependent ASR model selection is available on arXiv! In this paper we propose to train a decision module, that allows, given an audio sample, to use the smallest sufficient model leading to a good transcription

You want to give audio abilities to your VLM without compromising its vision performance? You want to align your audio encoder with a pretrained image encoder without suffering from the modality gap? Check our #NeurIPS2024 paper with Michel Olvera Stéphane LATHUILIÈRE and Slim Essid

New paper out - accepted at #ICCV2025 We introduce MoSiC, a self-supervised learning framework that learns temporally consistent representations from video using motion cues. Key idea: leverage long-range point tracks to enforce dense feature coherence across time.🧵

1/ New & old work on self-supervised representation learning (SSL) with ViTs: MOCA ☕ - Predicting Masked Online Codebook Assignments w/ Spyros Gidaris Oriane Siméoni Antonín Vobecký Matthieu Cord N. Komodakis, P. Pérez #TMLR #ICLR2025 Grab a ☕ and brace for a story & a 🧵