Progressive Localization Network for Text-to-Video Clip Segmentation
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Solution Overview
Problem
Existing methods for text-based video clip localization struggle to balance the generation of candidate clips with varying lengths, as larger step sizes result in coarser granularity, making it difficult to handle shorter clips, while smaller step sizes lead to redundant information and hinder optimization.
Innovation Solution
A multi-stage coarse-to-fine progressive localization network that uses different step sizes in multiple stages to generate candidate clips with varying time granularities, employing a Conditional Feature Update module and up-sampling connection to transfer learning information from coarse to fine time granularity branches, enhancing localization performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a larger step size is used for video segmentation, then the time granularity of generated clips becomes coarser and model optimization is improved, but the ability to cope with shorter target clips deteriorates
Solution Approach 1:
The patent divides the video segmentation process into multiple stages with different step sizes. The first stage uses a larger step size to generate coarse-grained candidate clips for efficient model optimization, while the second stage uses a smaller step size to generate fine-grained candidate clips that can accurately capture shorter target clips. This multi-stage segmentation approach resolves the contradiction by applying different granularity levels to different processing stages.
Solution Approach 2:
The patent introduces a temporal dimension by processing clips at multiple time scales. Instead of using a single step size, the system generates candidate clips at both coarse and fine temporal granularities, allowing the model to operate in a multi-scale temporal space that simultaneously accommodates both short and long clip requirements while maintaining optimization efficiency.
2Adaptability or versatility
If a smaller step size is used for video segmentation, then the time granularity of generated clips becomes finer and the ability to cope with shorter target clips is improved, but redundant information increases and model optimization is hindered
Solution Approach 1:
The patent segments the candidate generation process into two distinct stages: a first stage with larger step size that produces fewer, coarser candidate clips for efficient optimization, and a second stage with smaller step size that produces more, finer candidate clips that capture detailed temporal information. This segmentation allows the system to benefit from both coarse and fine granularities without the drawbacks of using either alone.
Solution Approach 2:
The patent generates more candidate clips than strictly necessary by using a smaller step size in the second stage, creating a superset of candidates that includes the optimal clip. This excessive generation of candidates is acceptable because the multi-stage approach filters down to the best candidates, ensuring that the target clip is captured with high precision while maintaining computational feasibility through progressive filtering.
Data Source
AI summary
A progressive localization method for text-to-video clip localization. The method comprises: first, respectively extracting features of two modes, namely a video mode and a text mode by using different feature extraction methods; then progressively selecting different step sizes, and learning the correlation between the video and the text in multiple stages; and finally, training a model in an end-to-end manner based on the correlation loss of each stage. Moreover, the fine time granularity stage is fused with information of the coarse time granularity stage by means of a condition feature update module and up-sampling connection, such that different stages are mutually promoted. Different stages can pay attention to clips with different time granularities, and the model can cope with the situation that the length of a target clip is obviously changed based on the interrelation between the stages.