Multimodal Video-Language Training for Fine-Grained ADL Understanding
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Solution Overview
Problem
Current Large Language Vision Models (LLVMs) struggle with fine-grained details and complex human object interactions in Activities of Daily Living (ADL) due to a lack of specialized video instruction-tuning datasets and insufficient modality integration.
Innovation Solution
A progressive training method is employed using video data with multiple modalities (video, object, and skeleton) and corresponding natural language labels, involving individual projection, combination, and alignment of these modalities into an embedding space with modality-specific connectors to enhance model understanding of ADL scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current LLVMs are trained on web videos, then general video understanding is achieved, but fine-grained details and complex human object interactions are not captured
Solution Approach 1:
The training process is segmented into multiple stages: first training on general web videos for broad understanding, then progressively training on specialized ADL datasets with multiple modalities (video, skeleton, object) to capture fine-grained details and complex interactions. This staged approach allows the model to build general capabilities before specializing in precise ADL understanding.
Solution Approach 2:
The patent combines multiple data modalities (video frames, skeleton data, object detection data) into a composite training dataset. This multi-modal composite approach enriches the training information to capture both fine-grained details and complex human-object interactions that single-modality datasets cannot provide.
2Measurement precision
If specialized ADL video instruction-tuning datasets are used, then fine-grained details and complex interactions are captured, but model complexity and training requirements increase
Solution Approach 1:
The model undergoes preliminary training on general web videos before being fine-tuned on specialized ADL datasets. This preliminary action establishes a solid foundation of general video understanding, reducing the complexity burden during subsequent specialized training and making the overall system more manageable.
Solution Approach 2:
The patent adds new dimensions to the training data by incorporating multiple modalities (video, skeleton, object) rather than relying solely on video frames. This dimensional enrichment captures complex interactions more effectively without necessarily increasing model architectural complexity, as the additional modalities provide complementary information.
3Loss of information
If multiple modalities are integrated, then discriminative action representations are captured, but data processing complexity increases
Solution Approach 1:
The patent merges multiple modalities (video, skeleton, object) into a unified training framework where all modalities are processed together to learn discriminative action representations. This merging approach ensures that the model captures rich multi-modal information while maintaining a coherent training process rather than handling each modality separately.
Data Source
AI summary
Described herein are apparatuses, methods, and computer program products for progressively training a model using video data comprising a plurality of modalities and corresponding natural language labels. The plurality of modalities comprise at least a video modality, an object modality, and a skeleton modality. Aa first stage includes individually projecting each of the video modality, the object modality, and the skeleton modality into an embedding space of the model. A second stage includes combining and projecting the video modality and the skeleton modality into the embedding space. A third stage includes combining and projecting the video modality, the object modality, and the skeleton modality into the embedding space. A language vision prediction system accesses the progressively trained model to ingest video data and to generate a natural language output associated with the video data.