Enhancing Action Localization with Human-in-the-Loop Corrections

In this article, the authors propose a new tool called ActLocalizer to help e-commerce platforms moderate videos more effectively and efficiently. The tool uses a combination of computer vision and machine learning techniques to identify and remove unwanted content from videos, while also considering factors like user engagement and business goals.
The authors explain that traditional video moderation methods are often manual and time-consuming, leading to missed content and low user satisfaction. To address this issue, ActLocalizer uses a storyline metaphor to visualize the alignment of actions in videos, making it easier for users to understand and manage the content.
The tool consists of two parts: a category list to represent action categories and a storyline to illustrate the actions and their alignments. The visual design is simple and easy to use, with a focus on legibility constraints to ensure that frames are placed adjacently while reducing line crossings, wiggles, and white space.
The authors also conduct four semi-structured interviews with experts in the field to gather feedback on the usability of ActLocalizer and identify areas for future improvement. The results show that the tool is user-friendly and effective, but there are still limitations that need to be addressed, such as the assumption of synchronized timestamps and location hierarchies for each timestamp.
Overall, ActLocalizer represents a significant advancement in the field of video moderation, providing a risk-aware framework that can help e-commerce platforms improve their content moderation processes while also improving user engagement and business goals.