Sequential Image Classification
37 papers with code • 3 benchmarks • 3 datasets
Sequential image classification is the task of classifying a sequence of images.
( Image credit: TensorFlow-101 )
Benchmarks
These leaderboards are used to track progress in Sequential Image Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Libraries
Use these libraries to find Sequential Image Classification models and implementations
CIFAR-10
MNIST
Mudestreda
Most implemented papers
Deep Independently Recurrent Neural Network (IndRNN)
Sunnydreamrain/IndRNN_pytorch
•
•
Recurrent neural networks (RNNs) are known to be difficult to train due to the gradient vanishing and exploding problems and thus difficult to learn long-term patterns and construct deep networks.
Improving the Gating Mechanism of Recurrent Neural Networks
aithlab/ImprovingGate
•
•
ICML 2020
Gating mechanisms are widely used in neural network models, where they allow gradients to backpropagate more easily through depth or time.
Recurrent Highway Networks with Grouped Auxiliary Memory
WilliamRo/gam_rhn
•
•
IEEE Access 2019
In this paper, we address these issues by proposing a novel RNN architecture based on RHN, namely the Recurrent Highway Network with Grouped Auxiliary Memory (GAM-RHN).
Lipschitz Recurrent Neural Networks
erichson/LipschitzRNN
•
•
ICLR 2021
Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-understood linear component plus a Lipschitz nonlinearity.
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
To effectively utilize the wealth of potential top-down information available, and to prevent the cacophony of intermixed signals in a bidirectional architecture, mechanisms are needed to restrict information flow.
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies
Circuits of biological neurons, such as in the functional parts of the brain can be modeled as networks of coupled oscillators.
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions.
CKConv: Continuous Kernel Convolution For Sequential Data
Convolutional networks are unable to handle sequences of unknown size and their memory horizon must be defined a priori.
Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition
Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade.
UnICORNN: A recurrent model for learning very long time dependencies
The design of recurrent neural networks (RNNs) to accurately process sequential inputs with long-time dependencies is very challenging on account of the exploding and vanishing gradient problem.
