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Representative large models and their related applications, such as Bidirectional encoder representations from transformers (BERT), Generative pretrained transformer (GPT), Segment anything model (SAM), ChatGPT, DALL-E, Wenxin, and Pangu, have made astounding strides and exerted considerable influence across various fields domestically and abroad. They constantly attract the attention and follow-up of diverse societal sectors, including enterprises, universities, and research institutions. Large model applications have been successfully applied in scenarios such as biology, medicine, law, and social governance. Designing, modifying, and constructing domain-specific large models are crucial for truly harnessing their application value. Therefore, this paper provides inspiration for the application of large models in materials science. First, it provides an overview of large models, introducing their basic concepts, developmental process, technical classification, and features. Second, from the perspectives of the general domain and specific large models, this paper summarizes the applications of large models and analyzes the application scenarios and functions of various types of large models. Subsequently, considering the specific needs and current state of research in the field of materials science, this paper reviews the application of large language models, large visual models, and large multimodal models. It integrates engineering strategies and zero-shot knowledge transfer learning from specific tasks in natural language processing and computer vision and referencing typical application cases, clarifying current research paradigms and limiting factors for applying large models to materials science. To verify the effectiveness and potential of the visual large model, basal experiments of image segmentation and key structure extraction are performed on the microscopic image data of four types of materials using improved SAM, including Ni-superalloy, superalloy, polycrystalline pure iron grain, and Inconel 939. The experimental results reveal that the zero-shot segmentation capability of SAM has enormous potential for accurate and efficient representation of material microstructures. With the help of tailored prompt engineering, precise masks of the precipitated phase, grain boundaries, and cracks can be outputted without any label. Finally, this paper proposes future research opportunities for technologies and methods related to large models in materials science. This paper assesses the feasibility and technical challenges for the development and tuning of unimodal to comprehensive multimodal large models. With continuous innovations and collaborations, the horizon for large models in materials science seems boundlessly promising. The integration of these models can produce a new era of advanced research, leading to advancements that were previously considered unattainable. The symbiosis between materials science and large models can pave the way for unforeseen discoveries, enriching our scientific prowess.

Person re-identification (REID) is an important task in video surveillance and forensics applications. Many previous works often build models on the assumption that they have same resolution cross different camera views, while it is divorced from reality. To increase the adaptability of person REID models, this paper focuses on the low-resolution person REID task to relax the impractical assumption when traditional low-resolution person REID models are under pixel-to-pixel supervision in low and high resolution pedestrian image pairs. In addition, they are easily influenced by the global background, illumination or pose variations across camera views. Therefore, we propose a Part-based Enhanced Super Resolution (PESR) network by employing a part division strategy and an enhanced generative adversarial network to boost the unpaired pedestrian image super resolution process. Specifically, the part-based super resolution network transforms low resolution image in probe into high resolution without any pixel-to-pixel supervision and the part-based synthetic feature extractor module can learn discriminative pedestrian feature representation for the generated high resolution images, which employ a part feature connection loss as constraint to conduct matching for person re-identification. Furthermore, evaluations on four public person REID datasets demonstrate the advantages of our method over the state-of-the-art ones.