Video Representation Learning With Multimodal Distillation
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Solution Overview
Problem
Existing video search technologies rely heavily on text or audio data, requiring large amounts of video data for network learning and incurring high costs, with poor quality input annotations and limited annotation descriptions.
Innovation Solution
An apparatus and method for video representation learning using a student network combined with teacher networks for video, image, and audio features, employing multi-model distillation and contrastive learning based on interdependence information, with a Siamese neural network generating positive and negative samples to train the student network effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing video search technologies use text or audio data selectively, then the system complexity is reduced, but the search accuracy and reliability deteriorate
Solution Approach 1:
The patent combines video, audio, and text data processing into a unified neural network system. The video encoder, audio encoder, and text encoder are integrated with a cross-modal attention mechanism that fuses features from all three modalities, allowing the system to leverage complementary information from multiple data types to improve search accuracy while maintaining manageable complexity through shared architectural components.
Solution Approach 2:
The patent implements a multi-functional neural network architecture where the same network structure processes video, audio, and text inputs. The cross-modal attention mechanism serves multiple purposes: it fuses features across modalities, handles different input types uniformly, and enables the system to adaptively weigh the importance of each modality based on the query, achieving both simplicity and high reliability.
2Measurement precision
If a large number of video data are used for network learning, then the learning accuracy is improved, but the training cost and time increase
Solution Approach 1:
The patent employs pre-training strategies where the neural network is first trained on large-scale video data to learn general video representations. The video encoder is pre-trained on extensive video datasets to capture temporal and spatial features, and the audio encoder is pre-trained on audio data. This preliminary action allows the model to achieve high learning accuracy on target tasks with less additional training time, as the heavy feature learning has already been performed during pre-training.
3Ease of operation
If text-based search methods are used, then the ease of operation is improved, but the annotation quality and search reliability deteriorate
Solution Approach 1:
The patent introduces a cross-modal attention mechanism as an intermediary between text queries and video content. Instead of directly matching text to video annotations, the system encodes the text query, processes video and audio data through encoders, and uses the attention mechanism to align and match features across modalities. This intermediary process preserves the ease of text-based operation while significantly improving reliability by leveraging rich video and audio features for more accurate matching.
Data Source
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AI summary
There are disclosed an apparatus and a method for video representation learning. An apparatus for video representation learning according to one embodiment may extract video features from video data to generate a video embedding, extract image features from image data extracted from the video data to generate an image embedding, and extract audio features from audio data extracted from the video data to generate an audio embedding. Further, contrastive learning may be performed by generating a first compositional embedding based on the video embedding and the audio embedding, generating a second compositional embedding based on the video embedding and the audio embedding, generating a positive sample and a negative sample based on a correlation between the image embedding and the audio embedding, and then using the data.