Video Retrieval Using Multi-Mentor Contrastive Learning
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Solution Overview
Problem
Current video retrieval techniques are limited by their reliance on discrete video frames, making it difficult to efficiently search and retrieve video clips.
Innovation Solution
The method involves training a neural network using a multi-mentor paradigm and contrastive learning, where video encoders are trained on pairs of video clips and associated text from different datasets, and a cosine similarity calculator is used to differentiate similarity and dissimilarity of video clips.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If discrete video frames are used for video retrieval, then the system structure is simple, but the retrieval accuracy and effectiveness deteriorate
Solution Approach 1:
The patent replaces traditional frame-based mechanical retrieval systems with a deep learning-based neural network system that processes video clips as continuous spatio-temporal data. The video encoder uses convolutional neural networks and attention mechanisms to capture temporal dependencies, substituting the discrete frame analysis with a continuous representation that maintains both structural simplicity and high retrieval accuracy
Solution Approach 2:
The patent changes the fundamental parameter of video representation from discrete frames to continuous spatio-temporal features. By transforming video data into a continuous feature space through neural network encoding, the system achieves both computational efficiency and high retrieval precision, resolving the contradiction between system simplicity and retrieval accuracy
2Use of energy by moving object
If traditional video retrieval methods are used, then the computational resources required are low, but the search efficiency and productivity deteriorate
Solution Approach 1:
The patent implements preliminary action by pre-training video encoders on large datasets to learn robust spatio-temporal feature representations. This pre-training enables the system to perform efficient retrieval with lower computational resources during actual search operations, as the heavy feature extraction work has already been performed during offline training
Solution Approach 2:
The patent substitutes traditional computational retrieval methods with neural network-based semantic search. The video encoder transforms visual data into semantic feature vectors that can be efficiently compared using cosine similarity, replacing computationally intensive frame-by-frame analysis with lightweight vector operations that achieve high search efficiency with minimal computational resources
Data Source
AI summary
A method, computer system, and a computer program product are provided for training a neural network for finding queried videos. Two pairs of video clips and associated text are obtained from a first dataset and a second dataset. The first dataset is used to train two video encoders by providing the video clips to the encoders as input and providing the outputs to a cosine similarity calculator. The second dataset is used to train a multi-mentor paradigm with two mentors. A first mentor and a second mentor are each provided the pair of textual data inputs. The first mentor provides a similarity value comparison, and the second mentor provides a word mover distance. Using the output from the multi-mentor paradigm and the encoders, a contrastive loss is calculated and used to provide contrastive learning of video features by differentiating similarity and dissimilarity of the video clips.