Extracting structured information from videos is critical for numerous
downstream applications in the industry. In this paper, we define a significant
task of extracting hierarchical key information from visual texts on videos. To
fulfill this task, we decouple it into four subtasks and introduce two
implementation solutions called PipVKIE and UniVKIE. PipVKIE sequentially
completes the four subtasks in continuous stages, while UniVKIE is improved by
unifying all the subtasks into one backbone. Both PipVKIE and UniVKIE leverage
multimodal information from vision, text, and coordinates for feature
representation. Extensive experiments on one well-defined dataset demonstrate
that our solutions can achieve remarkable performance and efficient inference
speed.
Extracting structured information from videos is a crucial task in the field of multimedia information systems. It has various applications in industries such as video analytics, content summarization, and video search. In this paper, the focus is on a specific task: extracting hierarchical key information from visual texts in videos.
The authors propose two implementation solutions called PipVKIE and UniVKIE. These solutions aim to tackle the task by breaking it down into four subtasks. PipVKIE follows a sequential approach, completing each subtask in continuous stages. On the other hand, UniVKIE takes a unified approach, combining all subtasks into a single backbone.
To represent features, both PipVKIE and UniVKIE leverage multimodal information from vision, text, and coordinates. This multi-disciplinary approach allows them to capture different aspects of the visual text, resulting in a more comprehensive representation of the hierarchical key information.
The authors conducted extensive experiments using a well-defined dataset to evaluate the performance and efficiency of their proposed solutions. The results show that both PipVKIE and UniVKIE achieve remarkable performance in terms of extracting hierarchical key information from visual texts in videos. Additionally, they demonstrate efficient inference speed, which is crucial for real-time applications.
From a wider perspective, this research aligns with the field of multimedia information systems. Multimedia information systems focus on managing and retrieving multimedia data, including videos, animations, and virtual realities. The task of extracting structured information from videos is a fundamental aspect of multimedia data analysis and retrieval.
Furthermore, the concepts presented in this paper have direct relevance to the fields of animations, artificial reality, augmented reality, and virtual realities. Animation involves creating visual texts that are often embedded within videos. By extracting hierarchical key information from these visual texts, it becomes easier to analyze and understand animated content.
Artificial reality, augmented reality, and virtual realities involve creating immersive and interactive experiences for users. The ability to extract structured information from videos, including visual texts, can enhance these experiences by providing relevant and context-aware information. For example, in augmented reality applications, the extracted hierarchical key information can be used to overlay additional textual information onto real-world objects, enhancing the user’s understanding and interaction with the environment.
In conclusion, the research presented in this paper contributes to the field of multimedia information systems by addressing the task of extracting hierarchical key information from visual texts in videos. The proposed solutions, PipVKIE and UniVKIE, leverage multimodal information and demonstrate remarkable performance and efficient inference speed. Furthermore, the concepts discussed have implications for animations, artificial reality, augmented reality, and virtual realities, enhancing multimedia experiences and applications in these domains.