Transformers,the dominant architecture for natural language processing,have also recently attracted much attention from computational visual media researchers due to their capacity for long-range representation and hi...Transformers,the dominant architecture for natural language processing,have also recently attracted much attention from computational visual media researchers due to their capacity for long-range representation and high performance.Transformers are sequence-to-sequence models,which use a selfattention mechanism rather than the RNN sequential structure.Thus,such models can be trained in parallel and can represent global information.This study comprehensively surveys recent visual transformer works.We categorize them according to task scenario:backbone design,high-level vision,low-level vision and generation,and multimodal learning.Their key ideas are also analyzed.Differing from previous surveys,we mainly focus on visual transformer methods in low-level vision and generation.The latest works on backbone design are also reviewed in detail.For ease of understanding,we precisely describe the main contributions of the latest works in the form of tables.As well as giving quantitative comparisons,we also present image results for low-level vision and generation tasks.Computational costs and source code links for various important works are also given in this survey to assist further development.展开更多
Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, p...Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.展开更多
基金supported by National Key R&D Program of China under Grant No.2020AAA0106200National Natural Science Foundation of China under Grant Nos.61832016 and U20B2070.
文摘Transformers,the dominant architecture for natural language processing,have also recently attracted much attention from computational visual media researchers due to their capacity for long-range representation and high performance.Transformers are sequence-to-sequence models,which use a selfattention mechanism rather than the RNN sequential structure.Thus,such models can be trained in parallel and can represent global information.This study comprehensively surveys recent visual transformer works.We categorize them according to task scenario:backbone design,high-level vision,low-level vision and generation,and multimodal learning.Their key ideas are also analyzed.Differing from previous surveys,we mainly focus on visual transformer methods in low-level vision and generation.The latest works on backbone design are also reviewed in detail.For ease of understanding,we precisely describe the main contributions of the latest works in the form of tables.As well as giving quantitative comparisons,we also present image results for low-level vision and generation tasks.Computational costs and source code links for various important works are also given in this survey to assist further development.
基金Project supported by the National Natural Science Foundation of China(Nos.62306075 and 62101136)the China Postdoctoral Science Foundation(No.2022TQ0069)+2 种基金the Natural Science Foundation of Shanghai,China(No.21ZR1403600)the Shanghai Municipal of Science and Technology Project,China(No.20JC1419500)the Shanghai Center for Brain Science and Brain-Inspired Technology,China。
文摘Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.
