Neural Language Model Action Anticipation via Textual Tag Prompts
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Solution Overview
Problem
Machine learning models face difficulties in predicting long-term human-object interactions due to the inherent ambiguity and unpredictability of human behavior, making it challenging to anticipate future actions in tasks like cooking or autonomous driving.
Innovation Solution
An electronic device employs neural language models to generate tags corresponding to sequences of actions from videos, using pre-trained models to predict subsequent actions by encoding prior knowledge and applying bottom-up or top-down approaches for accurate anticipation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning models are used for action anticipation, then the system can process video data, but the prediction accuracy deteriorates due to inherent ambiguity and unpredictability of human behavior
Solution Approach 1:
The patent introduces an intermediate textual representation layer (tags and prompts) between video input and action prediction output. The video is first converted to textual tags, which then serve as prompts for the language model to generate predicted actions. This intermediary textual layer helps bridge the gap between visual data and action prediction, improving accuracy by leveraging the structured nature of language to handle behavioral ambiguity.
2Measurement precision
If detailed video analysis is performed to improve prediction accuracy, then the system can capture more behavioral nuances, but the computational complexity increases
Solution Approach 1:
The patent extracts only the essential information from video data by converting it to a compact set of textual tags. Instead of processing the entire video stream with complex models, the system extracts key action-related tags that serve as inputs to the language model. This extraction approach maintains prediction accuracy while significantly reducing computational complexity by focusing only on relevant features.
3Loss of information
If the system processes complete video sequences to predict future actions, then the prediction context is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary conversion of video data to textual tags before the actual action prediction step. By pre-processing the video into a compact textual representation, the system retains essential context information in an efficient format that can be quickly processed by the language model. This preliminary action of tag generation reduces the time required for the final prediction while maintaining context integrity.
Data Source
AI summary
An electronic device and method for using neural language models for long-term action anticipation from videos is provided. The electronic device receives a video that includes one or more objects performing a physical task and generates, based on the video, a first set of tags that corresponds to a first sequence of actions associated with the physical task. The electronic device generates a first prompt for a neural language model based on the first set of tags and predicts, by application of the neural language model on the first prompt, a second set of tags that corresponds to a second sequence of actions associated with the physical task. The second sequence of actions succeeds the first sequence of actions. The electronic device controls a display device to display first prediction information based on the second set of tags.