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visual_question_answering_relation-networks_film's Introduction

Santoro et al. A simple neural network module for relational reasoning. NIPS, 2017. (in PyTorch)

Perez et al. FiLM: Visual Reasoning with a General Conditioning Layer. AAAI, 2018 (in PyTorch)

Description

This repository implements and reproduces two VQA models on Sort-of-CLEVR:

  • Relation Networks (RNs)
  • FiLM

Get Started

To use this repo, please download the dataset (Sort-of-CLEVR) below "/data" directory

Train/Eval

Train

  • To train RNs
./scripts/train_RNs.sh
  • To train FiLM
./scripts/train_FiLM.sh

Eval

For evaluation, trained model-weights should be located at "./models/here".

  • To evaluate RNs
./scripts/eval_RNs.sh
  • To evaluate FiLM
./scripts/eval_FiLM.sh

Benchmark (Sort-of-CLEVR)

Below is benchmark result. For fair comparison on relational reasoning ability, I used same CNN visual feature extractor for both models.

models #param Accuracy (relational qst)[%] Accuracy (non-relational qst)[%]
RNs 1.38M 92 99
FiLM 1.72M 94 99

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