There are 12 repositories under attention-model topic.
DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral
Attention mechanism Implementation for Keras.
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
Sequence-to-sequence framework with a focus on Neural Machine Translation based on PyTorch
Text classification using deep learning models in Pytorch
Implementation of the Swin Transformer in PyTorch.
A Structured Self-attentive Sentence Embedding
AttentionGAN for Unpaired Image-to-Image Translation & Multi-Domain Image-to-Image Translation
:punch: CV中常用注意力模块;即插即用模块;ViT模型. PyTorch Implementation Collection of Attention Module and Plug&Play Module
Attention OCR Based On Tensorflow
Camouflaged Object Detection, CVPR 2020 (Oral)
A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
This repository implements the the encoder and decoder model with attention model for OCR
PyTorch implementation of batched bi-RNN encoder and attention-decoder.
Code/Model release for NIPS 2017 paper "Attentional Pooling for Action Recognition"
Bidirectional LSTM network for speech emotion recognition.
attention model for entailment on SNLI corpus implemented in Tensorflow and Keras
A neural network to generate captions for an image using CNN and RNN with BEAM Search.
code of Relation Classification via Multi-Level Attention CNNs
Code & data accompanying the NAACL 2019 paper "Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases"
Soft attention mechanism for video caption generation
A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)
A Keras-based library for analysis of time series data using deep learning algorithms.
Seq2SeqSharp is a tensor based fast & flexible encoder-decoder deep neural network framework written by .NET (C#). It has many highlighted features, such as automatic differentiation, many different types of encoders/decoders(Transformer, LSTM, BiLSTM and so on), multi-GPUs supported and so on.
ECG Classification
Image Captioning based on Bottom-Up and Top-Down Attention model
This repository contain various types of attention mechanism like Bahdanau , Soft attention , Additive Attention , Hierarchical Attention etc in Pytorch, Tensorflow, Keras
Pytorch implementation of Unsupervised Attention-guided Image-to-Image Translation.
Deep Visual Attention Prediction (TIP18)
The implementation of "Gated Attentive-Autoencoder for Content-Aware Recommendation"
The implementation of "Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence"
Chatbot in russian with speech recognition using PocketSphinx and speech synthesis using RHVoice. The AttentionSeq2Seq model is used. Imlemented using Python3+TensorFlow+Keras.
Official Pytorch Implementation of our paper: Video Person Re-ID : Fantastic Techniques and Where to Find Them