bottom-up-attention

Attention model training

Trains a bottom-up attention model using Faster R-CNN and Visual Genome annotations for image captioning and VQA tasks

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

GitHub

1k stars
26 watching
378 forks
Language: Jupyter Notebook
last commit: over 3 years ago
Linked from 1 awesome list

caffecaptioning-imagesfaster-rcnnimage-captioningmscocomscoco-datasetvisual-question-answeringvqa

Backlinks from these awesome lists:

Related projects:

RepositoryDescriptionStars
hengyuan-hu/bottom-up-attention-vqaAn implementation of a VQA system using bottom-up attention, aiming to improve the efficiency and speed of visual question answering tasks.755
gt-vision-lab/vqa_lstm_cnnA Visual Question Answering model using a deeper LSTM and normalized CNN architecture.377
pistony/residualattentionnetworkA Gluon implementation of Residual Attention Network for image classification tasks108
jiasenlu/adaptiveattentionAdaptive attention mechanism for image captioning using visual sentinels335
zcyang/imageqa-sanThis project provides code for training image question answering models using stacked attention networks and convolutional neural networks.108
kaushalshetty/structured-self-attentionA deep learning model that generates sentence embeddings using structured self-attention and is used for binary and multiclass classification tasks.494
koichiro11/residual-attention-networkAn image classification neural network implementation using attention mechanisms and residual learning94
jessemelpolio/faster_rcnn_for_dotaThis repository provides code for training a Faster R-CNN object detection model on DOTA datasets.337
bigballon/cifar-zooProvides implementations of CNN architectures and improvement methods for image classification on the CIFAR benchmark.703
chapternewscu/image-captioning-with-semantic-attentionA deep learning model for generating image captions with semantic attention51
cszn/ircnnThis project trains deep CNN denoisers to improve image restoration tasks such as deblurring and demosaicking through model-based optimization methods.602
fwang91/residual-attention-networkAn implementation of a deep neural network architecture using attention mechanisms and residual connections for image classification tasks.554
emedvedev/attention-ocrA TensorFlow model for recognizing text in images using visual attention and a sequence-to-sequence architecture.1,079
cadene/vqa.pytorchA PyTorch implementation of visual question answering with multimodal representation learning718
szagoruyko/attention-transferImproves performance of convolutional neural networks by transferring knowledge from teacher models to student models using attention mechanisms.1,449