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  • SCOT: Self-Supervised Contrastive Pretraining For Zero-Shot . . .
    In this work, we propose SCOT (Self-supervised COmpositional Training), a novel zero-shot compositional pretraining strategy that combines existing large image-text pair datasets with the generative capabilities of large language models to contrastively train an embedding composition network
  • SCOT: Self-Supervised Contrastive Pretraining For Zero-Shot . . .
    In this work, we propose SCOT (Self-supervised COmpositional Training), a novel zero-shot compositional pretraining strategy that combines ex-isting large image-text pair datasets with the generative ca-pabilities of large language models to contrastively train an embedding composition network
  • SCOT: Self-Supervised Contrastive Pretraining For Zero-Shot . . .
    In this work, we propose SCOT (Self-supervised COmpositional Training), a novel zero-shot compositional pretraining strategy that combines existing large image-text pair datasets with the generative capabilities of large language models to contrastively train an embedding composition network
  • GitHub - bhavinjawade SCOT: SCOT: Self-Supervised Contrastive . . .
    SCOT: Self-Supervised Contrastive Pretraining For Zero-Shot Compositional Retrieval - bhavinjawade SCOT
  • SCOT: Self-Supervised Contrastive Pretraining For Zero-Shot . . .
    In this work, we propose SCOT (Self-supervised COmpositional Training), a novel zero-shot compositional pretraining strategy that combines existing large image-text pair datasets
  • SCOT: Self-Supervised Contrastive Pretraining for Zero-Shot . . .
    In this work, we propose SCOT (Self-supervised COmpositional Training), a novel zero-shot compositional pretraining strategy that combines existing large image-text pair datasets with the generative capabilities of large language models to contrastively train an embedding composition network
  • SCOT: Self-Supervised Contrastive Pretraining For Zero . . .
    现有的 CIR 方法主要依赖于完全监督学习，需要大量的人工标注的三元组数据集（如 FashionIQ 和 CIRR），这不仅劳动密集，而且模型缺乏对未见对象和领域的泛化能力。 此外，这些方法在处理未见领域的零样本场景时表现不佳。 为了解决这些问题，本文提出了一种新颖的自监督对比预训练策略，利用现有的大规模图像-文本对数据集和大型语言模型（LLM）的生成能力，对比训练一个嵌入组合网络。 这种方法不需要人工标注的三元组数据集，并且能够通过对比预训练的图像-文本编码器实现对未见领域的泛化。 1 自监督组合预训练 SCOT 的核心创新点在于利用对比预训练模型将相关的视觉和文本表示在嵌入空间中对齐，从而在训练期间消除对目标图像的需求。 具体来说，SCOT 通过以下步骤实现：
  • [论文评述] SCOT: Self-Supervised Contrastive Pretraining For Zero-Shot . . .
    这篇论文的标题是《SCOT: Self-Supervised Contrastive Pretraining For Zero-Shot Compositional Retrieval》，作者们主要提出了一种新的方法SCOT（自监督对比预训练），旨在解决传统图像检索方法在面对未见物体和领域时的普适性问题。
  • SCOT: Self-Supervised Contrastive Pretraining for Zero-Shot . . .
    SCOT: Self-Supervised Contrastive Pretraining for Zero-Shot Compositional Retrieval In IEEE CVF Winter Conference on Applications of Computer Vision, WACV 2025, Tucson, AZ, USA, February 26 - March 6, 2025 pages 5509-5519, IEEE, 2025 [doi]
  • SCOT: Self-Supervised Contrastive Pretraining For Zero-Shot . . .
    In Section 4 4, an ablation was conducted to examine the effect of varying the contrastively-trained encoders on zero-shot compositional retrieval performance on the FashionIQ and CIRR datasets





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