Sequential Image Classification Edit Task
Computer Vision • Image Classification
About
Sequential image classification is the task of classifying a sequence of images.
( Image credit: TensorFlow-101 )
Benchmarks
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Greatest papers with code
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• locuslab/TCN
• 
Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory.
Ranked #2 on
Music Modeling
on Nottingham
LANGUAGE MODELLING MACHINE TRANSLATION MUSIC MODELING SEQUENTIAL IMAGE CLASSIFICATION
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
CVPR 2018
• TobiasLee/Text-Classification
• 
Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly.
Ranked #7 on
Sequential Image Classification
on Sequential MNIST
LANGUAGE MODELLING SEQUENTIAL IMAGE CLASSIFICATION SKELETON BASED ACTION RECOGNITION
Trellis Networks for Sequence Modeling
ICLR 2019
• locuslab/trellisnet
•
On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices.
Dilated Recurrent Neural Networks
NeurIPS 2017
• code-terminator/DilatedRNN
•
To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.
Ranked #9 on
Sequential Image Classification
on Sequential MNIST
R-Transformer: Recurrent Neural Network Enhanced Transformer
ICLR 2020
• DSE-MSU/R-transformer
•
Recurrent Neural Networks have long been the dominating choice for sequence modeling.
Ranked #1 on
Music Modeling
on Nottingham
LANGUAGE MODELLING MUSIC MODELING SEQUENTIAL IMAGE CLASSIFICATION
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
NeurIPS 2019
• abr/neurips2019
•
Backpropagation through the ODE solver allows each layer to adapt its internal time-step, enabling the network to learn task-relevant time-scales.
Ranked #4 on
Sequential Image Classification
on Sequential MNIST
SEQUENTIAL IMAGE CLASSIFICATION TIME SERIES TIME SERIES PREDICTION
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.
Ranked #4 on
Sequential Image Classification
on Sequential MNIST
LANGUAGE MODELLING SEQUENTIAL IMAGE CLASSIFICATION SKELETON BASED ACTION RECOGNITION
Recurrent Batch Normalization
• cooijmanstim/recurrent-batch-normalization
• 
We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks.
Ranked #8 on
Sequential Image Classification
on Sequential MNIST
LANGUAGE MODELLING QUESTION ANSWERING READING COMPREHENSION SEQUENTIAL IMAGE CLASSIFICATION
Full-Capacity Unitary Recurrent Neural Networks
To address this question, we propose full-capacity uRNNs that optimize their recurrence matrix over all unitary matrices, leading to significantly improved performance over uRNNs that use a restricted-capacity recurrence matrix.
Ranked #10 on
Sequential Image Classification
on Sequential MNIST
Unitary Evolution Recurrent Neural Networks
When the eigenvalues of the hidden to hidden weight matrix deviate from absolute value 1, optimization becomes difficult due to the well studied issue of vanishing and exploding gradients, especially when trying to learn long-term dependencies.
Ranked #11 on
Sequential Image Classification
on Sequential MNIST