Versatile Action Models for Video Understanding via LLM Next Token Prediction
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Solution Overview
Problem
Existing generative models for human actions in video understanding struggle with capturing diverse and temporal dynamics of actions, requiring task-specific prior knowledge and failing to generalize to open vocabulary scenarios.
Innovation Solution
The method involves forming versatile action models by gathering data from videos, converting visual inputs into discrete action labels and free-form descriptions, and using a pre-trained large language model for next token prediction to unify video dynamic modeling tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rule-based generative action grammars are used, then the model structure is simple and interpretable, but the model cannot capture diverse and peculiar ways of how events unfold over time
Solution Approach 1:
The patent replaces rule-based generative action grammars with a transformer-based autoregressive model that processes visual tokens. This substitution allows the model to capture diverse and peculiar action patterns through data-driven learning rather than predefined rules, while maintaining computational efficiency through the transformer architecture's parallel processing capabilities.
Solution Approach 2:
The patent changes the model's approach from fixed rule-based parameters to data-driven learned parameters. By training the transformer model on diverse video data, the model automatically learns action patterns and temporal dynamics without requiring explicit rule definitions, enabling it to generalize to novel and peculiar action sequences.
2Adaptability or versatility
If data-driven autoregressive models are applied to visual tokens, then the model can capture temporal dynamics and generalize to open vocabulary, but the visual domain becomes specialized and requires task-specific knowledge
Solution Approach 1:
The patent creates a universal action model that can handle multiple video understanding tasks (action recognition, temporal reasoning, future prediction) through a single transformer-based architecture. The model's autoregressive nature allows it to process visual tokens and generate predictions across different domains without requiring task-specific retraining, achieving multi-functionality through one unified model.
Solution Approach 2:
The patent employs pre-training on large-scale video data to equip the model with general action patterns and temporal dynamics before applying it to specific tasks. This preliminary learning enables the model to handle open vocabulary scenarios and generalize to novel actions without requiring extensive task-specific training data or knowledge.
3Reliability
If manually defined actions and objects are used, then the model has task-specific prior knowledge, but it cannot generalize to open vocabulary scenarios in the wild
Solution Approach 1:
The patent transitions from manually defined action and object categories to automatically learned visual token representations. The transformer model processes visual tokens through learned embeddings and attention mechanisms, enabling it to generalize to open vocabulary scenarios while maintaining reliable performance on common actions through data-driven pattern recognition rather than manual definition.
Data Source
AI summary
A method for forming versatile action models for video understanding may gather data from a video. The data may comprise textual video representations and other task specific language inputs. The method may use a pre-trained large language model (LLM) next token prediction for action anticipation based on the data from the video.