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An efficient PyTorch implementation of the winning entry of the 2017 VQA Challenge.
A PyTorch reimplementation of bottom-up-attention models
Image captioning with Transformer
Visual Question Answering using Transformer and Bottom-Up attention. Implemented in Pytorch
Extract features and bounding boxes using the original Bottom-up Attention Faster-RCNN in a few lines of Python code
The main goal of is to show how precise the Faster R-CNN with ResNet-101 could find objects and there attributes in Conceptual 12m dataset.