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Sequential Image Classification

Sequential image classification is the task of classifying a sequence of images.

( Image credit: TensorFlow-101 )

Papers

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Showing 1–44 of 44 papers

TitleStatusHype
Minion Gated Recurrent Unit for Continual Learning—0
Learning Long Sequences in Spiking Neural Networks—0
Delayed Memory Unit: Modelling Temporal Dependency Through Delay GateCode0
Traveling Waves Encode the Recent Past and Enhance Sequence LearningCode1
Sequence Modeling with Multiresolution Convolutional MemoryCode1
SMPConv: Self-moving Point Representations for Continuous ConvolutionCode1
Resurrecting Recurrent Neural Networks for Long SequencesCode1
VCI-LSTM: Vector Choquet Integral-based Long Short-Term Memory—0
Image Classification using Sequence of Pixels—0
Efficient recurrent architectures through activity sparsity and sparse back-propagation through timeCode1
Efficiently Modeling Long Sequences with Structured State SpacesCode1
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space LayersCode0
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
Long Expressive Memory for Sequence ModelingCode1
RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural NetworksCode0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers—0
UnICORNN: A recurrent model for learning very long time dependenciesCode1
Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place RecognitionCode1
Parallelizing Legendre Memory Unit TrainingCode1
CKConv: Continuous Kernel Convolution For Sequential DataCode1
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place RecognitionCode1
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependenciesCode1
HiPPO: Recurrent Memory with Optimal Polynomial ProjectionsCode1
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over ModulesCode1
Lipschitz Recurrent Neural NetworksCode1
Learning Long-Term Dependencies in Irregularly-Sampled Time SeriesCode1
Recurrent Highway Networks with Grouped Auxiliary MemoryCode0
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural NetworksCode1
Gating Revisited: Deep Multi-layer RNNs That Can Be TrainedCode0
Improving the Gating Mechanism of Recurrent Neural NetworksCode0
Deep Independently Recurrent Neural Network (IndRNN)Code0
R-Transformer: Recurrent Neural Network Enhanced TransformerCode0
AntisymmetricRNN: A Dynamical System View on Recurrent Neural NetworksCode0
Learning to Remember More with Less MemorizationCode1
Trellis Networks for Sequence ModelingCode0
Long short-term memory and learning-to-learn in networks of spiking neuronsCode0
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNNCode0
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
Cortical microcircuits as gated-recurrent neural networks—0
Dilated Recurrent Neural NetworksCode0
Full-Capacity Unitary Recurrent Neural NetworksCode0
Recurrent Batch NormalizationCode0
Unitary Evolution Recurrent Neural NetworksCode0
A Simple Way to Initialize Recurrent Networks of Rectified Linear UnitsCode0
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