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vqa-mfb

bottom-up-attention

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

pytorch-pretrained-BERT

A PyTorch implementation of Google AI's BERT model with script to load Google's pre-trained models

openvqa

A lightweight, scalable, and general framework for visual question ansering (VQA) research

bottom-up-features

Bottom-up features extractor implemented in PyTorch.

finetune-transformer-lm

Code and model for the paper "Improving Language Understanding by Generative Pre-Training"

FPN

Feature Pyramid Networks for Object Detection

mcan-vqa

Deep Modular Co-Attention Networks for Visual Question Answering

activitynet-qa

An VideoQA dataset based on the videos from ActivityNet

caffe

Caffe: a fast open framework for deep learning.

DDPN

Rethinking Diversified and Discriminative Proposal Generation for Visual Grounding

Deformable-ConvNets-caffe

Deformable Convolutional Networks on caffe

Detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

Detectron.pytorch

A pytorch implementation of Detectron. Both training from scratch and inferring directly from pretrained Detectron weights are available.

py-R-FCN-multiGPU

Code for training py-faster-rcnn and py-R-FCN on multiple GPUs in caffe

pythia

A modular framework for Visual Question Answering research by the FAIR A-STAR team

R-FCN-PSROIAlign

A Caffe implementation of PSROI-Align

seg_every_thing

Code release for R. Hu et al., Learning to Segment Every Thing. in CVPR, 2018.