118 citations · 219 across the 8 of their papers we have counts for
8 papers
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…
Toward a Diffusion-Based Generalist for Dense Vision Tasks
Yue Fan, Yongqin Xian, Xiaohua Zhai +4
Building generalized models that can solve many computer vision tasks simultaneously is an intriguing direction. Recent works have shown image itself can be used as a natural inter…
CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?
Ibrahim Alabdulmohsin, Xiao Wang, Andreas Steiner +3
We study the effectiveness of data-balancing for mitigating biases in contrastive language-image pretraining (CLIP), identifying areas of strength and limitation. First, we reaffir…
PaLI-3 Vision Language Models: Smaller, Faster, Stronger
Xi Chen, Xiao Wang, Lucas Beyer +16
This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10x larger. As part of arriving at this s…
PaLI-X: On Scaling up a Multilingual Vision and Language Model
Xi Chen, Josip Djolonga, Piotr Padlewski +40
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training t…
A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision
Lucas Beyer, Bo Wan, Gagan Madan +9
There has been a recent explosion of computer vision models which perform many tasks and are composed of an image encoder (usually a ViT) and an autoregressive decoder (usually a T…