2 citations · 2 across the 1 of their papers we have counts for
8 papers
Scaling Pre-training to One Hundred Billion Data for Vision Language Models
Xiao Wang, Ibrahim Alabdulmohsin, Daniel Salz +3
We provide an empirical investigation of the potential of pre-training vision-language models on an unprecedented scale: 100 billion examples. We find that model performance tends…
A Tale of Two Structures: Do LLMs Capture the Fractal Complexity of Language?
Ibrahim Alabdulmohsin, Andreas Steiner
Language exhibits a fractal structure in its information-theoretic complexity (i.e. bits per token), with self-similarity across scales and long-range dependence (LRD). In this wor…
Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems
Ibrahim Alabdulmohsin, Xiaohua Zhai
Inspired by recent findings on the fractal geometry of language, we introduce Recursive INference Scaling (RINS) as a complementary, plug-in recipe for scaling inference time in la…
SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
Michael Tschannen, Alexey Gritsenko, Xiao Wang +11
We introduce SigLIP 2, a family of new multilingual vision-language encoders that build on the success of the original SigLIP. In this second iteration, we extend the original imag…
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…
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…