In multimodal learning, Vision-Language Models (VLMs) have become a critical research focus, enabling the integration of textual and visual data. These models have shown significant promise across various natural lang...In multimodal learning, Vision-Language Models (VLMs) have become a critical research focus, enabling the integration of textual and visual data. These models have shown significant promise across various natural language processing tasks, such as visual question answering and computer vision applications, including image captioning and image-text retrieval, highlighting their adaptability for complex, multimodal datasets. In this work, we review the landscape of Bootstrapping Language-Image Pre-training (BLIP) and other VLM techniques. A comparative analysis is conducted to assess VLMs’ strengths, limitations, and applicability across tasks while examining challenges such as scalability, data quality, and fine-tuning complexities. The work concludes by outlining potential future directions in VLM research, focusing on enhancing model interpretability, addressing ethical implications, and advancing multimodal integration in real-world applications.展开更多
利用纳米压痕仪的连续刚度测量模式测试了常温氙离子辐照后Hastelloy N合金的纳米硬度。结果表明,辐照样品的纳米硬度均大于未辐照样品的纳米硬度,且辐照剂量在0.5~3.0 dpa这一范围内时,辐照样品的纳米硬度处于饱和状态。在Nix-Gao模型...利用纳米压痕仪的连续刚度测量模式测试了常温氙离子辐照后Hastelloy N合金的纳米硬度。结果表明,辐照样品的纳米硬度均大于未辐照样品的纳米硬度,且辐照剂量在0.5~3.0 dpa这一范围内时,辐照样品的纳米硬度处于饱和状态。在Nix-Gao模型的基础上,分离出未辐照样品和辐照样品的压痕尺寸效应,并通过VLM(volume law of mixture)模型来模拟实验测得的纳米硬度。由于随着压头压入深度的增加,塑性影响区中将同时包含辐照损伤层与基体,在VLM模型中引入“界面参数”(χ)以修正基体的形变量,改进后的模型能够更好地模拟纳米压痕的实验结果。展开更多
文摘In multimodal learning, Vision-Language Models (VLMs) have become a critical research focus, enabling the integration of textual and visual data. These models have shown significant promise across various natural language processing tasks, such as visual question answering and computer vision applications, including image captioning and image-text retrieval, highlighting their adaptability for complex, multimodal datasets. In this work, we review the landscape of Bootstrapping Language-Image Pre-training (BLIP) and other VLM techniques. A comparative analysis is conducted to assess VLMs’ strengths, limitations, and applicability across tasks while examining challenges such as scalability, data quality, and fine-tuning complexities. The work concludes by outlining potential future directions in VLM research, focusing on enhancing model interpretability, addressing ethical implications, and advancing multimodal integration in real-world applications.
文摘利用纳米压痕仪的连续刚度测量模式测试了常温氙离子辐照后Hastelloy N合金的纳米硬度。结果表明,辐照样品的纳米硬度均大于未辐照样品的纳米硬度,且辐照剂量在0.5~3.0 dpa这一范围内时,辐照样品的纳米硬度处于饱和状态。在Nix-Gao模型的基础上,分离出未辐照样品和辐照样品的压痕尺寸效应,并通过VLM(volume law of mixture)模型来模拟实验测得的纳米硬度。由于随着压头压入深度的增加,塑性影响区中将同时包含辐照损伤层与基体,在VLM模型中引入“界面参数”(χ)以修正基体的形变量,改进后的模型能够更好地模拟纳米压痕的实验结果。
