Adaptive Video Frame Sampling for Multimodal Retrieval
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Solution Overview
Problem
Existing video-and-language learning methods face inefficiencies due to dense frame sampling, leading to computational overhead and inclusion of uninformative frames, which affects the performance of downstream multimodal tasks.
Innovation Solution
An adaptive sampling technique using a sampler neural network to select salient frames based on feature extraction and temporal dependencies, combined with a multimodal neural network trained on an aggregated loss that balances accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dense sampling at high frame rate is used, then video representation completeness is improved, but computational overhead increases
Solution Approach 1:
The patent applies partial action by selectively processing only a subset of frames (salient frames) rather than all frames. The sampler network identifies and processes approximately 30% of frames that contain meaningful information, discarding redundant frames. This partial processing approach maintains video representation completeness while significantly reducing computational overhead.
Solution Approach 2:
The patent applies local quality by treating different frames differently based on their information content. Instead of uniform processing, the system identifies frames with meaningful changes (salient frames) and processes them with higher priority, while skipping or reducing processing of redundant frames. This localized quality approach optimizes the balance between completeness and computational efficiency.
2Device complexity
If uniform sampling at low frame rate is used, then computational overhead is reduced, but performance in downstream multimodal tasks deteriorates
Solution Approach 1:
The patent uses partial action to process only the necessary subset of frames (salient frames) rather than uniform sampling. This selective processing ensures that all meaningful information is captured for downstream tasks while avoiding the computational overhead of processing every frame uniformly. The sampler network dynamically determines which frames are necessary for task performance.
Solution Approach 2:
The patent applies dynamics by making the frame sampling rate adaptive rather than static. The sampler network dynamically adjusts which frames to process based on the actual content and information density of each frame. This dynamic approach ensures optimal performance in downstream tasks while adapting computational resources to actual needs rather than using fixed uniform sampling.
3Loss of information
If all frames are processed, then information completeness is improved, but training and inference time increases
Solution Approach 1:
The patent applies partial action by processing only salient frames that contain meaningful information rather than all frames. The sampler network identifies frames with actual information content and processes only those, discarding redundant frames. This approach maintains information completeness for the necessary content while significantly reducing training and inference time.
Solution Approach 2:
The patent extracts and processes only the essential information-carrying frames from the full video sequence. The sampler network separates salient frames from redundant frames, extracting only the necessary subset for processing. This extraction approach preserves all critical information while eliminating time-wasting processing of unnecessary frames.
4Speed
If conventional sampling approaches are used, then processing speed is improved, but accuracy in video retrieval deteriorates due to noisy frames
Solution Approach 1:
The patent applies local quality by differentiating between salient and non-salient frames, applying different processing qualities to different frames. Salient frames that contribute to retrieval accuracy are processed with higher priority and detail, while noisy non-salient frames are skipped or processed minimally. This localized quality control maintains processing speed while improving retrieval accuracy by focusing resources on informative frames.
Solution Approach 2:
The patent uses partial action to process only the subset of frames that actually contribute to retrieval accuracy (salient frames). By identifying and processing only these frames rather than all frames uniformly, the system maintains processing speed while eliminating the negative impact of noisy, uninformative frames on retrieval accuracy.
Data Source
AI summary
An electronic device may obtain a query from a user input; obtain a sequence of frames of one or more input videos; select frames from the sequence of frames of the one or more input videos, via a sampler neural network configured to extract features from the sequence of frames that are input to the sampler neural network, determine temporal dependencies between the extracted features, and determine an action of selecting or skipping for each of the sequence of frames; and identify a video that matches the query via a multimodal neural network configured to receive the selected frames and the query, and output the video that matches the query, among the one or more input videos, wherein the sampler neural network and the multimodal neural network are jointly trained based on an aggregated loss that combines an accuracy loss that represents an accuracy of determining the video that matches the query, and an efficiency loss that reflects a proportion of frames being passed to the multimodal neural network.