Video Description Accuracy via Encoder-Decoder-Reconstructor Feedback
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Solution Overview
Problem
Existing video content understanding services face challenges in improving the accuracy of natural sentence descriptions for video content, leading to suboptimal quality and user experience due to neglect of partial semantics during feature extraction and prediction.
Innovation Solution
A video processing system incorporating an encoder, decoder, and reconstructor that utilizes bilateral information to enhance description accuracy by reconstructing video features and adjusting processing parameters to minimize differences between original and target video features, thereby improving the accuracy of natural sentence descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only encoding and decoding stages are used to generate natural sentences, then the processing speed is maintained, but the description accuracy rate is insufficient
Solution Approach 1:
The patent embeds the reconstructor module within the existing encoder-decoder framework. The reconstructor reconstructs video features from decoded features and provides feedback to the encoder, creating a nested structure where the feedback mechanism is integrated within the original processing pipeline, thus improving accuracy without proportionally increasing system complexity
Solution Approach 2:
The patent introduces a feedback mechanism where the reconstructor generates reconstructed video features that are compared with original video features, and the difference (feedback signal) is used to adjust encoder parameters. This feedback loop enables iterative optimization of feature extraction, thereby improving description accuracy rate
2Reliability
If video features are extracted without reconstruction feedback, then the processing efficiency is high, but the quality of natural sentence description deteriorates
Solution Approach 1:
The patent performs preliminary reconstruction of video features using the reconstructor before final natural sentence generation. By pre-reconstructing features and identifying discrepancies early in the process, the system can adjust encoder parameters in advance, ensuring higher description quality without significant time penalty during the main processing workflow
3Loss of information
If the encoder extracts features from all video frames, then the completeness of video content understanding is improved, but the computational complexity increases
Solution Approach 1:
The patent extracts only the essential and discriminative features from video frames through the encoder, rather than processing all raw frame data. The reconstructor further refines these extracted features by comparing reconstructed vs. original features, thereby maintaining complete content understanding while reducing computational energy through selective feature extraction and refinement
Data Source
AI summary
Provided are a video retrieval method and apparatus, a medium, and a server. The video retrieval method includes: receiving a video retrieval request from a terminal, the video retrieval request carrying retrieval key information; searching a video database for a natural sentence matching the retrieval key information, the video database including a plurality of videos, each video having a natural sentence obtained through processing frames of the video using an encoder, a decoder and a reconstructor of a neural network; obtaining a video corresponding to the natural sentence matching the retrieval key information; and returning the matching video to the requesting terminal.