Talk on group meeting about Egocentric Video/Image Quality Assessment
Talk, The internal group meeting, Dalian, China
This study report primarily focuses on the aesthetic assessment of images and first-person videos, presented by Li Zhizhen. The following is a summary of the main content: Introduction: The significance of aesthetic assessment and the research background are introduced. Transition from Traditional to Deep Learning: Traditional aesthetic assessment methods (such as average brightness, brightness contrast, global edge distribution, etc.) are discussed in comparison to deep learning methods, highlighting the advantages of deep learning in image aesthetic assessment. Relevant Datasets for Image Aesthetic Assessment: Various datasets, such as AADB and AVA, are introduced, comparing their characteristics, including rater IDs, the proportion of real photographs, and attribute labels. Analysis of Similarities and Differences in Aesthetic Assessment between Images and First-Person Videos: An analysis of the similarities and differences in aesthetic assessment between images and first-person videos is conducted, pointing out the uniqueness of first-person videos. Ego4D Dataset and Aesthetic Assessment Strategy for First-Person Videos: The characteristics of the Ego4D dataset are presented, including its geographical coverage and diversity of activities, along with five challenging tasks such as situational memory and future prediction. Conclusion and Future Outlook: The findings of the research are summarized, and future research directions in the field of aesthetic assessment are anticipated. Overall, this report explores the integration of traditional methods and deep learning in aesthetic assessment for both images and videos, emphasizing the significance of datasets and the potential for future research. You can visit PPT here.