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Sca Cnn.cvpr17Image Captions Generation with Spatial and Channel-wise Attention
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Bottom Up AttentionBottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome
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Image Caption GeneratorA neural network to generate captions for an image using CNN and RNN with BEAM Search.
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Neural Image CaptioningImplementation of Neural Image Captioning model using Keras with Theano backend
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Image CaptioningImage Captioning using InceptionV3 and beam search
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Graph attention poolAttention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)
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Yolov3 Point从零开始学习YOLOv3教程解读代码+注意力模块(SE,SPP,RFB etc)
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Csa InpaintingCoherent Semantic Attention for image inpainting(ICCV 2019)
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Up Down CaptionerAutomatic image captioning model based on Caffe, using features from bottom-up attention.
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Da Rnn📃 **Unofficial** PyTorch Implementation of DA-RNN (arXiv:1704.02971)
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Linear Attention Recurrent Neural NetworkA 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)
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Image Caption Generator[DEPRECATED] A Neural Network based generative model for captioning images using Tensorflow
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Image-CaptionUsing LSTM or Transformer to solve Image Captioning in Pytorch
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Abstractive SummarizationImplementation of abstractive summarization using LSTM in the encoder-decoder architecture with local attention.
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AoanetCode for paper "Attention on Attention for Image Captioning". ICCV 2019
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PdlsrPandas-aware non-linear least squares regression using Lmfit
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Python And Spark For Data AnalysisA four-day course on Python, the Scientific Python stack and PySpark, adapted from a training course given by Patrick Varilly to one of our clients in December 2015
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Changepoint DetectionOnline Change-point Detection Algorithm for Multi-Variate Data: Applications on Human/Robot Demonstrations.
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