PaLI-3 Vision Language Models: Smaller, Faster, Stronger
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 strong performance, we compare Vision Transformer
(ViT) models pretrained using classification objectives to contrastively
(SigLIP) pretrained ones. We find that, while slightly underperforming on
standard image classification benchmarks, SigLIP-based PaLI shows superior
performance across various multimodal benchmarks, especially on localization
and visually-situated text understanding. We scale the SigLIP image encoder up
to 2 billion parameters, and achieves a new state-of-the-art on multilingual
cross-modal retrieval. We hope that PaLI-3, at only 5B parameters, rekindles
research on fundamental pieces of complex VLMs, and could fuel a new generation
of scaled-up models.
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Xi Chen
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Xiao Wang
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Lucas Beyer
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2024-01-09T14:25:56.488Z
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Lucas Beyer
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Alexander Kolesnikov
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Jialin Wu
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Paul Voigtlaender
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Basil Mustafa
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Sebastian Goodman
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Ibrahim Alabdulmohsin
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Piotr Padlewski
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Daniel Salz
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2025-09-26T12:30:54.781Z
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Daniel Salz
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Xi Xiong
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Daniel Vlasic
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Filip Pavetic
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Keran Rong
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2025-02-13T08:28:22.875Z
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Keran 
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Tianli Yu
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Daniel Keysers
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2024-07-11T10:05:29.144Z
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Daniel Keysers
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Xiaohua Zhai
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Radu Soricut
PaLI-3 Vision Language Models: Smaller, Faster, Stronger
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 strong performance, we compare Vision Transformer
(ViT) models pretrained using classification objectives to contrastively
(SigLIP) pretrained ones. We find that, while slightly underperforming on
standard image classification benchmarks, SigLIP-based PaLI shows superior
performance across various multimodal benchmarks, especially on localization
and visually-situated text understanding. We scale the SigLIP image encoder up
to 2 billion parameters, and achieves a new state-of-the-art on multilingual
cross-modal retrieval. We hope that PaLI-3, at only 5B parameters, rekindles
research on fundamental pieces of complex VLMs, and could fuel a new generation
of scaled-up models.
PaLI-3, a smaller vision language model, outperforms larger models on multimodal tasks, particularly localization and text understanding, using SigLIP pretraining and achieves state-of-the-art results in multilingual cross-modal retrieval.
Vision Transformer SigLIP image encoder multilingual cross-modal retrieval localization visually-situated text understanding