Sequential Image Classification
37 papers with code • 3 benchmarks • 3 datasets
Sequential image classification is the task of classifying a sequence of images.
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Use these libraries to find Sequential Image Classification models and implementationsLatest papers with no code
Learning Long Sequences in Spiking Neural Networks
Spiking neural networks (SNNs) take inspiration from the brain to enable energy-efficient computations.
Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate
Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling temporal dependencies, a trait that has driven their widespread adoption for sequential data processing.
VCI-LSTM: Vector Choquet Integral-based Long Short-Term Memory
Choquet integral is a widely used aggregation oper- ator on one-dimensional and interval-valued information, since it is able to take into account the possible interaction among data.
Image Classification using Sequence of Pixels
This study compares sequential image classification methods based on recurrent neural networks.
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers
Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency.
Cortical microcircuits as gated-recurrent neural networks
Cortical circuits exhibit intricate recurrent architectures that are remarkably similar across different brain areas.