Model design for multi-modal tasks

点击翻译按钮获取中文摘要

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.