Model design for multi-modal tasks

近年来，人工智能为各个领域的重大进步铺平了道路，特别是随着生成模型的出现。生成模型以其产生高度逼真内容的卓越能力而闻名，在从文本生成[1]到图像合成[2]的广泛应用中表现出非凡的潜力。然而，为了进一步提高这些生成模型在现实世界场景中的性能和适应性，我们必须探索创新技术，以促进高效和可扩展的模型专业化。值得注意的是，当代生成模型往往负担着密集的计算需求，缺乏专门处理特定任务的灵活性，从而限制了它们在不同领域的效用。同时，我们目睹了各种生成模型的显着增长，这些模型针对特定任务、场景和领域进行了预训练或微调。由于这些预训练的生成模型已经具备了特定任务的某些能力，因此如何利用它们来定制更复杂的任务已经成为一个紧迫的问题。为了应对这些挑战，本研究旨在使这些模型能够适应个性化的专业化，以适应个体研究者和实践者的不同需求。本研究将遵循以下具体目标和潜在的研究方向：（1）生成模型的全面探索：我们将对生成模型的现状进行全面检查。我们的调查将包括对它们的应用程序、优势以及优化可以提高效率的领域的详细分析。(2)多样模型编辑[3]和融合[4]技术的研究：我们将开始探索各种模型编辑和融合技术，目的是了解它们在增强生成模型能力方面的潜力。这些技术旨在降低与模型个性化相关的训练成本，并通过与来自不同模态的不同模型融合来增强其性能。(3)模型专业化策略的创新和评估：特别关注具有挑战性的任务，例如涉及具体代理的任务，我们将构思和评估来自我们提出的模型编辑和融合技术的模型专业化的创新策略。目标是将联合收割机不同的模型结合起来，并采用自动搜索不同的模型[5]，以有效地处理复杂的现实世界任务。总之，本研究旨在为生成模型的发展做出有意义的贡献，特别强调其效率和适应性。我们的工作与EPSRC职权范围内的人工智能和机器人主题领域保持一致，旨在提供有价值的工具，以有效地定制生成模型以满足独特的需求和要求。因此，它们可以应用于广泛的应用，包括诸如医学图像生成、药物发现、图像超分辨率和游戏内容生成等领域。金，Z. M.，& Kang，D.（2023年）。自然语言处理中的扩散模型：综述。arXiv预印本arXiv：2305.14671。[2]龙巴赫河，巴西-地布拉特曼，A.，洛伦茨，D.，埃塞尔角& Ommer，B.（2022年）。利用潜在扩散模型的高分辨率图像合成。在IEEE/CVF关于计算机视觉和模式识别的会议记录中（第10684-10695）。[3]Mitchell，E.，林芝，Bosselut，A.，Finn，C.，& Manning，C. D.（2021年）。大规模快速模型编辑。arXiv预印本arXiv：2110.11309。[4]辛格，S。P.，& Jaggi，M.（2020年）。通过最佳传输进行模型融合。神经信息处理系统的进展，33，22045-22055。[5]重要的重力。AutoGPT [计算机软件]。https://github.com/Significant-Gravitas/AutoGPT.

In recent years, artificial intelligence has paved the way for significant advancements across various domains, particularly with the advent of generative models. Generative models, known for their remarkable ability to produce highly realistic content, have demonstrated exceptional potential in a wide range of applications, ranging from text generation [1] to image synthesis [2]. However, to further enhance the performance and adaptability of these generative models in real-world scenarios, it is imperative that we explore innovative techniques to facilitate efficient and scalable model specialization. Notably, contemporary generative models often come burdened with intensive computational requirements and lack the flexibility to specialize in specific tasks, thereby limiting their utility across diverse domains.Simultaneously, we have witnessed a remarkable growth of diverse generative models, pretrained or fine-tuned for specific tasks, scenarios, and domains. As these pretrained generative models are already equipped with certain abilities for specific tasks, the question of how to leverage them for customization to more complex tasks has become a pressing concern. To address these challenges, this research aims to render these models amenable to personalized specialization, accommodating the diverse requirements of individual researchers and practitioners.This research will be guided by the following specific objectives and potential research directions: (1) Comprehensive Exploration of Generative Models: We will conduct a thorough examination of the current landscape of generative models. Our investigation will encompass a detailed analysis of their applications, strengths, and areas where optimization can enhance their efficiency. (2) Investigation of Diverse Model Editing [3] and Fusion [4] Techniques: We will embark on an exploration of various model editing and fusion techniques with the aim of comprehending their potential in augmenting the capabilities of generative models. These techniques are intended to reduce the training costs associated with model personalization and enhance their performance through fusion with diverse models from different modalities. (3) Innovation and Evaluation of Model Specialization Strategies: With a specific focus on challenging tasks, such as those involving embodied agents, we will conceive and assess innovative strategies for model specialization derived from our proposed model editing and fusion techniques. The goal is to combine different models and employ automated search for different models [5] to tackle complex real-world tasks effectively.In summary, this research seeks to make a meaningful contribution to the evolving landscape of generative models, with a specific emphasis on their efficiency and adaptability. Our work is in alignment with the artificial intelligence and robotics thematic area in the EPSRC's remit, which aims to provide valuable tools to efficiently tailor generative models to unique needs and requirements. Thus, they can be applied to a wide spectrum of applications, including fields such as medical image generation, drug discovery, image super-resolution, and content generation for games and so on. [1] Zou, H., Kim, Z. M., & Kang, D. (2023). Diffusion models in nlp: A survey. arXiv preprint arXiv:2305.14671.[2] Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10684-10695).[3] Mitchell, E., Lin, C., Bosselut, A., Finn, C., & Manning, C. D. (2021). Fast model editing at scale. arXiv preprint arXiv:2110.11309.[4] Singh, S. P., & Jaggi, M. (2020). Model fusion via optimal transport. Advances in Neural Information Processing Systems, 33, 22045-22055.[5] Significant Gravitas. AutoGPT [Computer software]. https://github.com/Significant-Gravitas/AutoGPT.