Video-Grounded Dialogue Spatial-Temporal Reasoning
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Solution Overview
Problem
Conventional video-grounded dialogue systems neglect spatial dimensions and struggle with reasoning over long videos involving multiple objects, as they primarily focus on temporal variance and neglect potential spatial signals.
Innovation Solution
A framework that formulates video-grounded dialogue tasks as sequence-to-sequence tasks, combining visual and textual representations into a structured sequence, and fine-tunes pre-trained neural network language models like GPT-2 to capture dependencies across spatial-temporal and token-sentence levels, incorporating spatial, temporal, and modality-level encodings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional video-grounded dialogue systems focus on temporal variance, then they can process video sequences, but they neglect spatial dimensions and struggle with reasoning over multiple objects
Solution Approach 1:
The patent merges spatial and temporal reasoning capabilities into a unified model architecture by integrating spatial encoding mechanisms with temporal sequence processing. This allows the system to simultaneously handle both spatial relationships between objects and temporal variations in video sequences, resolving the contradiction between enhanced adaptability and model complexity through integrated design rather than separate modules
Solution Approach 2:
The patent introduces spatial dimension encoding into the existing temporal processing framework by adding spatial position embeddings and spatial relationship features to the temporal sequence models. This dimensional enhancement enables the system to reason about both spatial and temporal aspects of video content without requiring a complete architectural overhaul, thus improving spatial reasoning capability while controlling complexity
2Measurement precision
If the model captures complex spatial-temporal dependencies, then reasoning accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the complex spatial-temporal reasoning task into distinct components: spatial encoding, temporal encoding, and interaction modeling. By dividing the processing into manageable segments with specialized encoders for each dimension, the system achieves high reasoning accuracy through focused processing while controlling computational complexity through modular architecture that avoids redundant computations
Solution Approach 2:
The patent applies preliminary encoding actions by pre-processing spatial and temporal features into compact representations before main reasoning operations. Spatial position embeddings and temporal feature extractions are performed in advance, creating condensed feature vectors that capture essential information while reducing the dimensionality and computational burden of subsequent reasoning tasks
3Duration of action of moving object
If the system processes long videos with multiple objects, then comprehensive analysis is achieved, but performance degrades due to neglecting spatial signals
Solution Approach 1:
The patent introduces spatial encoding mechanisms as intermediary components between raw video input and temporal processing. These spatial encoders act as mediators that extract and represent spatial relationships between objects before temporal analysis, ensuring that spatial signals are preserved and integrated throughout the processing of long video sequences, thus maintaining response reliability
Data Source
AI summary
A system and method for generating a response in a video grounded dialogue are provided. A video-grounded dialogue neural network language model receives video input and text input. The text input includes a dialogue history between the model and a human user and a current utterance by the user. Encoded video input is generated using video encoding layers. Encoded text input is generated using text encoding layers. The encoded video input and the encoded text input are concatenated in to a single input sequence. A generative pre-trained transformer model generates the response to the current utterance from the singe input sequence.