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Computer Science > Computer Vision and Pattern Recognition

arXiv:2303.15343 (cs)
[Submitted on 27 Mar 2023 (v1), last revised 27 Sep 2023 (this
version, v4)]

Title:Sigmoid Loss for Language Image Pre-Training

Authors:Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas
Beyer
Download a PDF of the paper titled Sigmoid Loss for Language Image
Pre-Training, by Xiaohua Zhai and 3 other authors
Download PDF

    Abstract:We propose a simple pairwise Sigmoid loss for
    Language-Image Pre-training (SigLIP). Unlike standard contrastive
    learning with softmax normalization, the sigmoid loss operates
    solely on image-text pairs and does not require a global view of
    the pairwise similarities for normalization. The sigmoid loss
    simultaneously allows further scaling up the batch size, while
    also performing better at smaller batch sizes. Combined with
    Locked-image Tuning, with only four TPUv4 chips, we train a
    SigLiT model that achieves 84.5% ImageNet zero-shot accuracy in
    two days. The disentanglement of the batch size from the loss
    further allows us to study the impact of examples vs pairs and
    negative to positive ratio. Finally, we push the batch size to
    the extreme, up to one million, and find that the benefits of
    growing batch size quickly diminish, with a more reasonable batch
    size of 32k being sufficient. We release our models at this https
    URL and hope our research motivates further explorations in
    improving the quality and efficiency of language-image
    pre-training.

          ICCV'23 Oral. arXiv v2: fix typo in pseudocode; v3: clarify
Comments: t vs t' init; v4: add SigLIP Base, Large, Shape-Optimized
          400M results. Models released at: this https URL. Xiaohua
          and Lucas contributed equally
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial
          Intelligence (cs.AI)
Cite as:  arXiv:2303.15343 [cs.CV]
          (or arXiv:2303.15343v4 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2303.15343
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Xiaohua Zhai [view email]
[v1] Mon, 27 Mar 2023 15:53:01 UTC (312 KB)
[v2] Thu, 30 Mar 2023 17:03:49 UTC (332 KB)
[v3] Thu, 4 May 2023 17:39:26 UTC (324 KB)
[v4] Wed, 27 Sep 2023 12:05:41 UTC (601 KB)
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    Image Pre-Training, by Xiaohua Zhai and 3 other authors
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