Displaying 1 to 7 from 7 results

CameraManager - Simple Swift class to provide all the configurations you need to create custom camera view in your app

  •    Swift

This is a simple Swift class to provide all the configurations you need to create custom camera view in your app. It follows orientation change and updates UI accordingly, supports front and rear camera selection, pinch to zoom, tap to focus, exposure slider, different flash modes, inputs and outputs. Just drag, drop and use. The Swift Package Manager is a tool for managing the distribution of Swift code.

show-attend-and-tell - TensorFlow Implementation of "Show, Attend and Tell"

  •    Jupyter

Update (December 2, 2016) TensorFlow implementation of Show, Attend and Tell: Neural Image Caption Generation with Visual Attention which introduces an attention based image caption generator. The model changes its attention to the relevant part of the image while it generates each word.First, clone this repo and pycocoevalcap in same directory.

neuralmonkey - An open-source tool for sequence learning in NLP built on TensorFlow.

  •    Python

The Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.

knowing-when-to-look - adaptive attention model

  •    Python

adaptive attention model. tensorflow implementation of knowing when to look: adaptive attention via visual sentinel for image captioning.

AdaptiveAttention - Implementation of "Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning"

  •    Jupyter

To train the model require GPU with 12GB Memory, if you do not have GPU, you can directly use the pretrained model for inference. This code is written in Lua and requires Torch. The preprocssinng code is in Python, and you need to install NLTK if you want to use NLTK to tokenize the caption.

captioning_chainer - A fast implementation of Neural Image Caption by Chainer

  •    Python

A Chainer implementation of Neural Image Caption, which generates captions given images. This implementation is fast, because it uses cudnn-based LSTM (NStepLSTM) and beam search can deal with batch processing.

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