As one of the key components of clinical trials, blinding, if successfully implemented, can help to mitigate the risks of implementation bias and measurement bias, consequently improving the validity and reliability o...As one of the key components of clinical trials, blinding, if successfully implemented, can help to mitigate the risks of implementation bias and measurement bias, consequently improving the validity and reliability of the trial results. However, successful blinding in clinical trials of traditional Chinese medicine(TCM) is hard to achieve, and the evaluation of blinding success through blinding assessment lacks established guidelines. Taking into account the challenges associated with blinding in the TCM field, here we present a framework for assessing blinding. Further, this study proposes a blinding assessment protocol for TCM clinical trials, building upon the framework and the existing methods. An assessment report checklist and an approach for evaluating the assessment results are presented based on the proposed protocol. It is anticipated that these improvements to blinding assessment will generate greater awareness among researchers, facilitate the standardization of blinding, and augment the blinding effectiveness. The use of this blinding assessment may further advance the quality and precision of TCM clinical trials and improve the accuracy of the trial results. The blinding assessment protocol will undergo continued optimization and refinement, drawing upon expert consensus and experience derived from clinical trials.展开更多
Image generation models have made remarkable progress,and image evaluation is crucial for explaining and driving the development of these models.Previous studies have extensively explored human and automatic evaluatio...Image generation models have made remarkable progress,and image evaluation is crucial for explaining and driving the development of these models.Previous studies have extensively explored human and automatic evaluations of image generation.Herein,these studies are comprehensively surveyed,specifically for two main parts:evaluation protocols and evaluation methods.First,10 image generation tasks are summarized with focus on their differences in evaluation aspects.Based on this,a novel protocol is proposed to cover human and automatic evaluation aspects required for various image generation tasks.Second,the review of automatic evaluation methods in the past five years is highlighted.To our knowledge,this paper presents the first comprehensive summary of human evaluation,encompassing evaluation methods,tools,details,and data analysis methods.Finally,the challenges and potential directions for image generation evaluation are discussed.We hope that this survey will help researchers develop a systematic understanding of image generation evaluation,stay updated with the latest advancements in the field,and encourage further research.展开更多
The exponential growth of video content has driven significant advancements in video summarization techniques in recent years.Breakthroughs in deep learning have been particularly transformative,enabling more effectiv...The exponential growth of video content has driven significant advancements in video summarization techniques in recent years.Breakthroughs in deep learning have been particularly transformative,enabling more effective detection of key information and creating new possibilities for video synopsis.To summarize recent progress and accelerate research in this field,this paper provides a comprehensive review of deep learning-based video summarization methods developed over the past decade.We begin by examining the research landscape of video abstraction technologies and identifying core challenges in video summarization.Subsequently,we systematically analyze prevailing deep learning frameworks and methodologies employed in current video summarization systems,offering researchers a clear roadmap of the field's evelution.Unlike previous review works,we first classify research papers based on the structural hierarchy of the video(from frame-level to shot-level to video-level),then further categorize them according to the summary backbone model(feature extraction and spatiotemporal modeling).This approach provides a more systematic and hierarchical organization of the documents.Following this comprehensive review,we summarize the benchmark datasets and evaluation metrics commonly employed in the field.Finally,we analyze persistent challenges and propose insightful directions for future research,providing a forward-looking perspective on video summarization technologies.This systematic literature review is of great reference value to new researchers exploring the fields of deep learning and video summarization.展开更多
基金supported by the National Natural Science Foundation of China(No.82174530).
文摘As one of the key components of clinical trials, blinding, if successfully implemented, can help to mitigate the risks of implementation bias and measurement bias, consequently improving the validity and reliability of the trial results. However, successful blinding in clinical trials of traditional Chinese medicine(TCM) is hard to achieve, and the evaluation of blinding success through blinding assessment lacks established guidelines. Taking into account the challenges associated with blinding in the TCM field, here we present a framework for assessing blinding. Further, this study proposes a blinding assessment protocol for TCM clinical trials, building upon the framework and the existing methods. An assessment report checklist and an approach for evaluating the assessment results are presented based on the proposed protocol. It is anticipated that these improvements to blinding assessment will generate greater awareness among researchers, facilitate the standardization of blinding, and augment the blinding effectiveness. The use of this blinding assessment may further advance the quality and precision of TCM clinical trials and improve the accuracy of the trial results. The blinding assessment protocol will undergo continued optimization and refinement, drawing upon expert consensus and experience derived from clinical trials.
基金supported by the National Natural Science Foundation of China(No.62006208)the Provincial Key Research and Development Plan of Zhejiang Province(No.2024C01250(SD2))。
文摘Image generation models have made remarkable progress,and image evaluation is crucial for explaining and driving the development of these models.Previous studies have extensively explored human and automatic evaluations of image generation.Herein,these studies are comprehensively surveyed,specifically for two main parts:evaluation protocols and evaluation methods.First,10 image generation tasks are summarized with focus on their differences in evaluation aspects.Based on this,a novel protocol is proposed to cover human and automatic evaluation aspects required for various image generation tasks.Second,the review of automatic evaluation methods in the past five years is highlighted.To our knowledge,this paper presents the first comprehensive summary of human evaluation,encompassing evaluation methods,tools,details,and data analysis methods.Finally,the challenges and potential directions for image generation evaluation are discussed.We hope that this survey will help researchers develop a systematic understanding of image generation evaluation,stay updated with the latest advancements in the field,and encourage further research.
基金supported by UKRI(EP/Z000025/1)Horizon Europe Programme under the MSCA grant for the ACMod project(101130271)。
文摘The exponential growth of video content has driven significant advancements in video summarization techniques in recent years.Breakthroughs in deep learning have been particularly transformative,enabling more effective detection of key information and creating new possibilities for video synopsis.To summarize recent progress and accelerate research in this field,this paper provides a comprehensive review of deep learning-based video summarization methods developed over the past decade.We begin by examining the research landscape of video abstraction technologies and identifying core challenges in video summarization.Subsequently,we systematically analyze prevailing deep learning frameworks and methodologies employed in current video summarization systems,offering researchers a clear roadmap of the field's evelution.Unlike previous review works,we first classify research papers based on the structural hierarchy of the video(from frame-level to shot-level to video-level),then further categorize them according to the summary backbone model(feature extraction and spatiotemporal modeling).This approach provides a more systematic and hierarchical organization of the documents.Following this comprehensive review,we summarize the benchmark datasets and evaluation metrics commonly employed in the field.Finally,we analyze persistent challenges and propose insightful directions for future research,providing a forward-looking perspective on video summarization technologies.This systematic literature review is of great reference value to new researchers exploring the fields of deep learning and video summarization.
