Convolutional Structured State Space Model for Parallel Spatiotemporal Sequence Processing
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Solution Overview
Problem
Conventional methods for modeling multi-dimensional sequence data, such as 3D CNNs, convolutional LSTM neural networks, and transformers, face challenges in preserving volumetric information, handling long-range dependencies, and achieving efficient parallelization, leading to slow training and scalability issues.
Innovation Solution
The convolutional structured state space model combines tensor modeling with state space methods, using convolutional linear state space operations and nonlinear activation functions to extend spatiotemporal sequences, allowing for easy parallelization in both spatial and temporal dimensions while preserving volumetric information and handling long-range dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If 3D convolutional neural networks are used to preserve volumetric information, then volumetric information is preserved, but the temporal context that can be captured is limited by the 3D kernel size, preventing handling of long-range dependencies
Solution Approach 1:
The patent transitions from 3D spatial convolution to 1D temporal convolution by reshaping the 3D state matrix into a 1D sequence. This dimensionality change allows the model to capture long-range temporal dependencies through 1D convolution while preserving volumetric information through the structured state space representation that maintains spatial relationships.
2Duration of action of moving object
If convolutional LSTM neural networks are used to preserve volumetric information and learn long-range dependencies, then volumetric information is preserved and long-range dependencies can be learned, but nonlinear functions in convolutional layers prevent parallelization across sequence length, leading to slow training
Solution Approach 1:
The patent extracts and removes the nonlinear activation functions from the convolutional layers, retaining only the linear convolutional operations. This extraction allows the model to maintain the ability to learn long-range dependencies through structured state space while enabling efficient parallelization across sequence lengths, dramatically improving training speed.
3Productivity
If transformer techniques are used to encode multi-dimensional inputs into vectors for 1D sequence models, then processing can be parallelized across sequence length, but volumetric information is not preserved and the approach relies on learning a useful compression
Solution Approach 1:
The patent performs preliminary action by reshaping the 3D state matrix into a 1D sequence before applying convolutional operations. This preliminary transformation enables parallelization while preserving volumetric information in the structured state space representation, avoiding the need for compression that would be required by transformer-based approaches.
4Adaptability or versatility
If recurrent neural networks are used to update tensor-valued states, then complex spatial correlations can be modeled, but sequential computation causes the recurrent neural networks to be slow to train
Solution Approach 1:
The patent inverts the conventional RNN approach by applying 1D convolutional operations to the temporal dimension of the state matrix instead of using sequential recurrent updates. This inversion transforms the sequential computation into parallelizable operations while maintaining the ability to model complex spatial correlations through the structured state space representation.
Data Source
AI summary
Systems and methods are disclosed related to a convolutional structured state space model (ConvSSM), which has a tensor-structured state but a continuous-time parameterization and linear state updates. The linearity may be exploited to use parallel scans for subquadratic parallelization across the spatiotemporal sequence. The ConvSSM effectively models long-range dependencies and, when followed by a nonlinear operation forms a spatiotemporal layer (ConvS5) that does not require compressing frames into tokens, can be efficiently parallelized across the sequence, provides an unbounded context, and enables fast autoregressive generation.