Weakly Supervised Video Mature Content Localization
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Solution Overview
Problem
Current methods for detecting mature content in videos, especially in long-form content, face challenges such as high computational and memory requirements and difficulty in capturing salient information due to irrelevant frames, leading to inefficient processing and reduced accuracy in identifying multiple instances of mature content.
Innovation Solution
A weakly temporal localization-based machine learning model using convolutional neural networks (CNNs) with fully connected layers for frame-level detection and a novel attention mechanism with sparsity loss to efficiently process long videos and accurately identify mature content, reducing irrelevant frames and focusing on relevant scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to detect mature content in long-form videos, then detection coverage is provided, but computational and memory requirements become excessively high
Solution Approach 1:
The video is segmented into multiple frames, and the detection model processes only salient frames identified through attention mechanisms rather than all frames uniformly. This segmentation allows the system to maintain comprehensive detection coverage while reducing computational and memory requirements by focusing resources on critical temporal segments.
Solution Approach 2:
The patent applies local quality by dynamically adjusting the processing intensity and model complexity based on the importance of individual frames. Through attention mechanisms, the system identifies and processes only the most relevant frames with higher computational resources while using lighter processing for less critical frames, thereby reducing overall computational and memory requirements while maintaining detection reliability.
2Measurement precision
If traditional methods process all frames uniformly, then complete video analysis is achieved, but salient information is lost due to irrelevant frames
Solution Approach 1:
The system performs preliminary actions by using attention mechanisms to pre-identify and prioritize salient frames before full detection processing. This preliminary screening of frames based on attention scores allows the system to focus computational resources on the most informative frames, improving salient information capture while reducing processing time by avoiding unnecessary analysis of irrelevant frames.
Solution Approach 2:
The patent maintains continuity of useful action by continuously refining frame selections through iterative attention mechanisms. The system continuously identifies and processes salient frames in a coordinated manner, ensuring that useful information is captured efficiently without the interruptions and redundancies of uniform frame processing, thereby improving measurement precision while optimizing processing time.
3Ease of manufacture
If video-level labels are used for training, then training data requirements are reduced, but temporal localization accuracy becomes challenging
Solution Approach 1:
The patent introduces attention mechanisms as intermediary components that bridge the gap between coarse video-level labels and fine-grained temporal localization. These attention mechanisms act as mediators that translate the information from video-level labels into frame-level attention scores, enabling the system to achieve accurate temporal localization even when trained only on video-level labels by interpolating temporal information through the attention weights.
Data Source
AI summary
Techniques for temporal localization of mature content in long-form videos using only video-level labels are described. According to some embodiments, computer-implemented method includes receiving a request to train a machine learning model on a training video file comprising at least one mature content label, training the machine learning model to generate a feature vector for each of a plurality of video frames of the training video file, generate a plurality of frame-level mature content classification scores of the training video file from the feature vectors of the training video file, and generate a video-level mature content classification score of the training video file from the plurality of frame-level mature content classification scores for the training video file based at least in part on the at least one mature content label of the training video file, receiving a request for an input video file, generating, by the machine learning model in response to the request, a feature vector for each of a plurality of video frames of the input video file, a plurality of frame-level mature content classification scores of the input video file from the feature vectors of the input video file, and a video-level mature content classification score of the input video file from the plurality of frame-level mature content classification scores for the input video file, and transmitting the plurality of frame-level mature content classification scores of the input video file or the video-level mature content classification score of the input video file to a client application or to a storage location.