With the deepening of research on Large Language Models (LLMs), significant progress has been made in recent years on the development of Large Multimodal Models (LMMs), which are gradually moving toward Artificial General Intelligence. This paper aims to summarize the recent progress from LLMs to LMMs in a comprehensive and unified way. First, we start with LLMs and outline various conceptual frameworks and key techniques. Then, we focus on the architectural components, training strategies, fine-tuning guidance, and prompt engineering of LMMs, and present a taxonomy of the latest vision–language LMMs. Finally, we provide a summary of both LLMs and LMMs from a unified perspective, make an analysis of the development status of large-scale models in the view of globalization, and offer potential research directions for large-scale models.

In the past decade, SIFT descriptor has been witnessed as one of the most robust local invariant feature descriptors and widely used in various vision tasks. Most traditional image-classification systems depend on the gray-based SIFT descriptors, which only analyze the gray level variations of the images. Misclassification may happen since their color contents are ignored. In this article, we concentrate on improving the performance of existing image-classification algorithms by adding color information. To achieve this purpose, different kinds of colored SIFT descriptors are introduced and implemented. locality-constrained linear coding (LLC), a state-of-the-art sparse coding technology, is employed to construct the image-classification system for the evaluation. Moreover, we propose a simple \(\ell _2\) -norm regularized local distance to improve the traditional LLC method. The real experiments are carried out on several benchmarks. With the enhancements to color SIFT and \(\ell _2\) -norm regularization, the proposed image-classification system obtains approximately \(2\,\%\) improvement of classification accuracy on the Caltech-101 dataset and approximately \(5\,\%\) improvement of classification accuracy on the Caltech-256 dataset.

Despite the effectiveness of convolutional neural networks (CNNs) especially in image classification tasks, the effect of convolution features on learned representations is still limited. It mostly focuses on the salient object of the images, but ignores the variation information on clutter and local. In this paper, we propose a special framework, which is the multiple vector of locally aggregated descriptors (VLAD) encoding method with the CNNs features for image classification. Furthermore, in order to improve the performance of the VLAD coding method, we explore the multiplicity of VLAD encoding with the extension of three kinds of encoding algorithms, which are the VLAD-SA method, the VLAD-LSA and the VLAD-LLC method. Finally, we equip the spatial pyramid patch (SPM) on VLAD encoding to add the spatial information of CNNs feature. In particular, the power of SPM leads our framework to yield better performance compared to the existing method.