danieljf24

keras-language-modeling

:book: Some language modeling tools for Keras

RecurrentHighwayNetworks

Recurrent Highway Networks - Implementations for Tensorflow, Torch7, Theano and Brainstorm

MemNN

Memory Networks implementations

skip-thoughts

Sent2Vec encoder and training code from the paper "Skip-Thought Vectors"

awesome-vqa

Visual Q&A reading list

MovieQA_CVPR2016

Contains approaches introduced in the MovieQA benchmark dataset paper

visual-qa

[Reimplementation Antol et al 2015] Keras-based LSTM/CNN models for Visual Question Answering

vqa-mcb

HieCoAttenVQA

VQA_LSTM_CNN

Train a deeper LSTM and normalized CNN Visual Question Answering model. This current code can get 58.16 on OpenEnded and 63.09 on Multiple-Choice on test-standard.

VQA_Demo

Visual Question Answering Demo on pretrained model

visual_turing_test-tutorial

Tutorial for Visual Turing Test (visual question answering, image question answering).

VQAbaseline

Simple Baseline for Visual Question Answering

moviepy

Video editing with Python

text-to-image

Text to image synthesis using thought vectors

show_and_tell.tensorflow

image2label

SVM based image concept recognition

models

Models and examples built with TensorFlow

tf-rnn

Practical Examples for RNNs in Tensorflow

mRNN-CR

XX-Net

A proxy tool to bypass GFW.

neural-image-captioning

Using scene-specific contexts and region-based attention in neural image captioning

SA-tensorflow

Soft attention mechanism for video caption generation

show_attend_and_tell.tensorflow

CNN-LSTM-Caption-Generator

A Tensorflow implementation of CNN-LSTM image caption generator architecture that achieves close to state-of-the-art results on the MSCOCO dataset.

TF-mRNN

Re-implementation of the m-RNN model using TensorFLow

cs231n

Convolutional Neural Networks for Visual Recognition in Stanford

tflearn

Deep learning library featuring a higher-level API for TensorFlow.

TensorFlow-Examples

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

visual-semantic-embedding

Implementation of the image-sentence embedding method described in "Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models"