Hierarchical Video Captioning Pipelines for Long-Range Scene Changes
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Solution Overview
Problem
Existing video generation models struggle to generate high-quality long videos due to the lack of high-quality training datasets, particularly for long-range dynamic scene changes, leading to temporal inconsistency and limited motion patterns.
Innovation Solution
A pipeline is developed that filters long videos from large-scale datasets using scene cut detection and optical flow techniques, followed by hierarchical captioning with generative multimodal models to generate temporally-dense captions, refining them into consolidated captions for training datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If ASR is used to extract text descriptions from videos to increase dataset scale, then the amount of data increases significantly, but the text descriptions fail to accurately represent the main video content
Solution Approach 1:
The video is divided into multiple segments, and each segment is processed independently to generate focused captions that accurately describe specific portions of the video content, improving overall caption precision while maintaining dataset scale
Solution Approach 2:
A hierarchical captioning system with multiple processing stages is introduced as an intermediary between the video input and final captions, allowing for refined and accurate description generation through progressive refinement
2Productivity
If models are trained on short video clips to enable long video generation using sliding window techniques, then training data requirements are met, but quality degradation and lack of temporal consistency occur
Solution Approach 1:
The video is pre-processed into overlapping segments with carefully designed transitions before captioning, establishing temporal relationships in advance that enable consistent long-video generation without quality degradation
Solution Approach 2:
Overlapping segments are used to maintain continuous temporal information flow across segment boundaries, ensuring that temporal consistency is preserved throughout the entire video duration during both training and generation phases
3Loss of information
If videos are partitioned into multiple segments for hierarchical captioning, then processing complexity increases, but comprehensive coverage of long-range dynamic scene changes is achieved
Solution Approach 1:
A nested hierarchical structure is employed where videos are divided into segments, which are further divided into smaller units, with each level contributing to comprehensive scene change coverage while organizing complexity in a manageable hierarchical manner
Data Source
AI summary
Techniques and associated pipelines for generating captions and annotations of videos are provided. One aspect includes a method for captioning a video, the method comprising: receiving the video to be captioned; partitioning the video into a plurality of segments; for each of the segments, generating an image grid comprising a plurality of frames in the segment; for each of the image grids, generating an image grid caption describing the image grid using a generative multimodal model; and generating a consolidated caption for the video using the generative multimodal model or a generative language model to consolidate the image grid captions.