Dense Video Captioning via Region-Sequence Language Modeling
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Solution Overview
Problem
Conventional video description technologies provide inadequate, superficial descriptions that fail to accurately capture the rich content of videos, limiting the effectiveness of video search and evaluation due to reliance on single sentence captions and lack of region-sequence level annotations.
Innovation Solution
A dense video captioning system that generates multiple sentence descriptions for each region of a video using a combination of visual, region-sequence, and language components, including a trained computational model for mapping words to video regions and a sequence-to-sequence learning framework for generating informative and diverse region-sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional one-sentence captioning is used for video description, then the system complexity is low, but the description accuracy and information completeness deteriorate
Solution Approach 1:
The patent segments the video description task into multiple components: region proposal generation, region feature extraction, and sentence generation. Instead of generating a single caption for the entire video, the system divides the video into multiple regions and generates descriptions for each region, thereby improving description accuracy while managing complexity through modular architecture
Solution Approach 2:
The patent transitions from a single-dimension approach (one sentence for entire video) to a multi-dimensional approach by introducing spatial dimension (regions within frames) and temporal dimension (sequences of regions across frames). This allows the system to capture rich video content through region-sequence level annotations, improving description completeness
2Loss of information
If region-sequence level annotations are used for dense video captioning, then the description completeness improves, but the annotation cost and computational expense increase
Solution Approach 1:
The patent performs preliminary region proposal generation using trained computational models before detailed annotation. By pre-identifying candidate regions and their sequences, the system reduces the manual annotation workload while maintaining region-sequence level detail, making dense video captioning more feasible
Solution Approach 2:
The patent uses trained computational models to generate region proposals and features that copy or replicate expert annotation quality. The models learn from annotated data and can automatically generate region-sequence level descriptions, reducing the need for expensive manual annotation while preserving information completeness
3Measurement precision
If global visual representation is used for video description, then the processing speed is fast, but the mapping accuracy from sentences to visual content deteriorates
Solution Approach 1:
The patent segments the global visual representation into region-level visual features. Instead of mapping sentences to a single global video representation, the system maps sentences to specific region sequences, improving the accuracy of the sentence-to-visual mapping while maintaining processing efficiency through localized feature extraction
Data Source
AI summary
Techniques and apparatus for generating dense natural language descriptions for video content are described. In one embodiment, for example, an apparatus may include at least one memory and logic, at least a portion of the logic comprised in hardware coupled to the at least one memory, the logic to receive a source video comprising a plurality of frames, determine a plurality of regions for each of the plurality of frames, generate at least one region-sequence connecting the determined plurality of regions, apply a language model to the at least one region-sequence to generate description information comprising a description of at least a portion of content of the source video. Other embodiments are described and claimed.