Adopts the Learning to Rank and Convolutional Neural Network (CNN) algorithms for tags to provide real-time recognition with high accuracy.
Supports customization and categorization of video qualities with the ability to pick up on figures, art and entertainment personalities, and even clothing styles.
Builds a comprehensive hierarchical tag library that can output general and fine-grained tags.
Analyzes videos from multiple dimensions, such as sound, action, image, and text, to fully understand the video content so that you can get more complete results.
Recommends related videos to suit user preference based on recognition and analysis of the video you are watching as well as degree of tag correlation between the video and other videos.
Comprehensively measures video content characteristics with multidimensional analysis capabilities, enhancing the accuracy of tag descriptions.
Yields high precision in tag recognition and recommends videos with high relevancy.
Accurately extracts valid and refined tags from the video content.
Recognizes actions in videos with frame analysis, optical flow, and scenario information output capabilities.
Integrates the image, optical flow, and sound information to provide more accurate action recognition.
Adopts the 3D CNN algorithm to achieve high accuracy in action recognition.
Ability to recognize actions even in complex scenarios with diverse weather conditions and varying camera angles so you can still get the analysis you need regardless of environment considerations.