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Systems for extracting features from sequences using bidirectional processing and gating.
Distinct from Gated Sequence Models: Distinct from Gated Sequence Models: specifically emphasizes the bidirectional processing of spatial image features.
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Vim is a state space model vision framework designed for image classification and visual representation learning. It functions as a computer vision research tool that converts two-dimensional image grids into one-dimensional sequences to extract spatial features. The system implements a linear-scaling image classifier that replaces quadratic attention mechanisms with state space operations. This approach utilizes bidirectional sequence modeling and selective gating mechanisms to process visual data. The framework covers computer vision benchmarking and image classification research, providin
Extracts spatial image features using bidirectional sequence modeling and selective gating.