Multimodal Video Retrieval With Dynamic Frame Selection
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Solution Overview
Problem
Existing multimodal video retrieval methods face issues with frame selection methods that are inflexible and resource-inefficient, leading to sub-optimal performance and computational waste due to the inclusion of uninformative frames, while frame aggregation methods fail to capture important content effectively.
Innovation Solution
A dynamic frame selection and aggregation process using a neural network model to filter out uninformative frames and determine a weighted representation of videos based on available resources, employing a mask to prevent re-selection and using a multilayer perceptron for efficient frame selection and aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform sampling is used to select frames, then the selection process is simple and flexible with wide coverage, but uninformative frames are selected introducing artifacts and wasting computational resources
Solution Approach 1:
The system employs a light-weight neural network model that automatically evaluates and selects informative frames from video sequences. The model processes frames independently and assigns importance scores, enabling self-service frame selection without requiring complex manual curation or extensive computational resources, thus resolving the contradiction between simplicity and reliability
Solution Approach 2:
The patent changes the parameter of frame selection from uniform random sampling to importance-based selection using a neural network. By introducing a scoring mechanism that evaluates frame informativeness, the system transforms the selection criterion while maintaining computational efficiency, thereby improving retrieval performance without sacrificing simplicity
2Ease of manufacture
If all frames are aggregated equally to form video representation, then the process is straightforward, but important content may be diluted by less important frames
Solution Approach 1:
The system applies local quality by assigning different weights to different frames based on their informativeness. The neural network model evaluates each frame individually and assigns importance scores, ensuring that more important frames contribute more to the video representation while less important frames contribute less, thereby capturing essential content without dilution
Solution Approach 2:
Instead of aggregating all frames equally, the system performs partial aggregation by selectively weighting frames based on their importance. The light-weight model identifies and emphasizes key frames that contain critical information, performing a focused aggregation that captures essential video content more effectively than uniform aggregation of all frames
3Ease of manufacture
If fixed proportion of frames are selected, then the method is consistent and simple, but it is inflexible to variability in computational resources
Solution Approach 1:
The system implements dynamic frame selection by using a light-weight neural network model that can adapt the number of selected frames based on available computational resources. The model processes frames and assigns importance scores dynamically, allowing the system to select an appropriate number of informative frames regardless of resource constraints, thus achieving both consistency in selection quality and flexibility in adaptation
Solution Approach 2:
The patent changes the selection parameter from a fixed proportion to a dynamic count based on resource availability. The light-weight model enables the system to adjust the number of frames selected according to computational constraints while maintaining quality through importance-based evaluation, resolving the contradiction between consistency and adaptability
Data Source
AI summary
Provided are system, method, and device for performing multimodal video retrieval. According to embodiments, the method may include: obtaining a first plurality of frames of a video; selecting a second plurality of frames from among the first plurality of frames using a frame selection module, wherein a number of the second plurality of frames may be less than a number of the first plurality of frames; determining a representation of the video based on the selected second plurality of frames using a neural network model; and storing the representation of the video in a memory.