160 citations · 409 across the 17 of their papers we have counts for
5 papers · 1 filter
Jet: A Modern Transformer-Based Normalizing Flow
Alexander Kolesnikov, André Susano Pinto, Michael Tschannen
In the past, normalizing generative flows have emerged as a promising class of generative models for natural images. This type of model has many modeling advantages: the ability to…
PaliGemma 2: A Family of Versatile VLMs for Transfer
Andreas Steiner, André Susano Pinto, Michael Tschannen +15
PaliGemma 2 is an upgrade of the PaliGemma open Vision-Language Model (VLM) based on the Gemma 2 family of language models. We combine the SigLIP-So400m vision encoder that was als…
JetFormer: An Autoregressive Generative Model of Raw Images and Text
Michael Tschannen, André Susano Pinto, Alexander Kolesnikov
Removing modeling constraints and unifying architectures across domains has been a key driver of the recent progress in training large multimodal models. However, most of these mod…
PaliGemma: A versatile 3B VLM for transfer
Lucas Beyer, Andreas Steiner, André Susano Pinto +32
PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly know…
LocCa: Visual Pretraining with Location-aware Captioners
Bo Wan, Michael Tschannen, Yongqin Xian +7
Image captioning has been shown as an effective pretraining method similar to contrastive pretraining. However, the incorporation of location-aware information into visual pretrain…