vqav2

There are 0 repository under vqav2 topic.

• rentainhe / TRAR-VQA

  [ICCV 2021] Official implementation of the paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering"

  vqav2iccv2021transformerclevrmulti-modalvision-and-languagevisual-question-answeringpytorchmulti-scale-featuresdynamic-networkattentionlocal-and-globalmulti-modalityvisualizationmulti-modal-learningofficial
  Language:Python 63

• vtu81 / NaiveVQA

  A Visual Question Answering model implemented in MindSpore and PyTorch. The model is a reimplementation of the paper *Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering*. It's our final project for course DL4NLP at ZJU.

  mindsporepytorchvqadeep-learningvqav2nlp
  Language:Jupyter Notebook 8

• phiyodr / vqaloader

  PyTorch DataLoader for many VQA datasets

  dataloadergqapytorchtextvqavqavqav2
  Language:Python 6

• williamcfrancis / Visual-Question-Answering-using-Stacked-Attention-Networks

  Pytorch implementation of VQA using Stacked Attention Networks: Multimodal architecture for image and question input, using CNN and LSTM, with stacked attention layer for improved accuracy (54.82%). Includes visualization of attention layers. Contributions welcome. Utilizes Visual VQA v2.0 dataset.

  computer-visiondeep-learningnatural-language-processingpytorchstacked-attention-networksvisual-question-answeringvqavqav2
  Language:Jupyter Notebook 5
• adaptively-finetuning-transformers

  itsShnik / adaptively-finetuning-transformers

  Adaptively fine tuning transformer based models for multiple domains and multiple tasks

  transformersfinetuningvlbertlxmertvision-and-languagevisual-question-answeringpytorchvqav2vqacpv2spottuneblockdrop
  Language:Python 4

• BrightQin / RWSAN

  Official implementation of "Deep Residual Weight-Sharing Attention Network with Low-Rank Attention for Visual Question Answering" (RWSAN) published in the IEEE Transactions on Multimedia (TMM), 2022.

  pytorchvqav2
  Language:Python 3

• rentainhe / TRAR-Feature-Extraction

  Grid features extraction for ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering"

  visual-question-answeringiccv2021extract-featurespytorchvqav2vqavqa-dataset
  Language:Python 2

• shreyas21563 / VQA-using-BLIP

  Leveraging the BLIP Model for Visual Question Answering: A Comparative Analysis on VQA and DAQUAR Datasets

  blipcomputer-visionimage-captioninginferencemachine-learningnatural-language-processingvisual-question-answeringvqav2daquaraccuracybert-scorebleu-scorewups
  Language:Jupyter Notebook