Video Captioning Accuracy via Scene Graph Feature Extraction
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Solution Overview
Problem
Existing video captioning models face challenges such as slow training speed, limited training sample diversity, neglect of intra-frame information, reliance on labor-intensive labeling, uncontrollable caption length, and poor accuracy due to limited datasets and lack of semantic and spatial-temporal features.
Innovation Solution
The method involves extracting characteristic information from multimedia data using networks like Faster R-CNN, semantic prediction networks, and graph convolution networks to generate captions, incorporating local visual, semantic, spatial-temporal, and attribute features, and using self-attention-based encoders and decoders to improve caption accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing video captioning models use traditional encoder-decoder structures with limited feature extraction, then the model complexity is low and training is simpler, but the captioning accuracy is poor and semantic understanding is limited
Solution Approach 1:
The patent segments the video captioning task into multiple specialized modules: a spatial-temporal feature extraction module that divides feature extraction into spatial (intra-frame) and temporal (inter-frame) components, a scene graph construction module that separates object detection, relationship extraction, and attribute recognition, and a multi-branch decoding module. This segmentation allows each module to specialize in specific aspects of video understanding, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent introduces spatial-temporal dimensions by constructing scene graphs that capture both spatial relationships (object positions, orientations within frames) and temporal relationships (object trajectories, interactions across frames). This dimensional expansion transforms the captioning task from simple frame analysis to comprehensive spatio-temporal reasoning, significantly improving semantic understanding and captioning accuracy.
2Measurement precision
If more training samples and diverse data are used to improve captioning accuracy, then the model performance improves, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-processing video data into structured scene graphs before training, extracting spatial relationships, temporal relationships, and object attributes in advance. This pre-structuring of data accelerates the training process as the model receives organized, semantically-rich inputs rather than raw video frames, reducing training time while maintaining high accuracy.
Solution Approach 2:
The patent employs parameter changes by implementing multi-scale feature extraction that processes video at different temporal resolutions and spatial detail levels. This allows the model to capture both fine-grained object attributes and coarse-grained temporal patterns efficiently, optimizing the balance between training speed and accuracy by adapting feature extraction parameters to different video segments.
3Productivity
If traditional frame selection methods are used to reduce processing load, then the processing speed is faster, but intra-frame information and spatial details are lost
Solution Approach 1:
The patent extracts and separately processes spatial features from each video frame using a dedicated spatial feature extraction module that operates on selected key frames. This extraction approach preserves important intra-frame information such as object positions, orientations, and visual attributes while still maintaining processing efficiency through selective frame sampling rather than processing every frame.
Solution Approach 2:
The patent merges spatial features (extracted from individual frames) with temporal features (derived from frame sequences) in a unified scene graph representation. This combination allows the model to retain detailed intra-frame spatial information while integrating it with inter-frame temporal context, achieving both high processing speed and comprehensive information retention through feature fusion.
4Adaptability or versatility
If fixed-length captions are generated using traditional decoding, then the decoding process is simpler and faster, but the caption length cannot be controlled and may miss important details
Solution Approach 1:
The patent implements dynamic caption length control through a multi-branch decoding mechanism that can adaptively adjust the number of decoding steps and caption length based on video content complexity and user requirements. Different decoding branches handle different caption length scenarios, allowing the system to generate concise or detailed captions dynamically without fixed constraints, enhancing adaptability while managing complexity through structured branching.
Data Source
AI summary
Embodiments of the present disclosure provide a method, an apparatus, a device, and a medium for generating captioning information of multimedia data. The method includes extracting characteristic information of multimedia data to be processed, wherein the multimedia data comprises a video or an image; and generating a text caption of the multimedia data based on the extracted characteristic information. According to the method provided in the embodiments of the present disclosure, the accuracy of the generated text caption of the multimedia data can be effectively improved.