Vision systems to see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range
of downstream tasks have gained significant interest lately in various deep-learning …

Is vision good enough for language? Recent advancements in multimodal models primarily
stem from the powerful reasoning abilities of large language models (LLMs). However the …

In the last year alone, a surge of new benchmarks to measure $\textit {compositional} $
understanding of vision-language models have permeated the machine learning ecosystem …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

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 …

We present the All-Seeing (AS) project: a large-scale data and model for recognizing and
understanding everything in the open world. Using a scalable data engine that incorporates …

Abstract We introduce Generative Infinite-Vocabulary Transformers (GIVT) which generate
vector sequences with real-valued entries, instead of discrete tokens from a finite …

Understanding long real-world videos requires modeling of long-range visual
dependencies. To this end we explore video-first architectures building on the common …

Image-Text pretraining on web-scale image caption datasets has become the default recipe
for open vocabulary classification and retrieval models thanks to the success of CLIP and its …