Stl Vqa
The good practice in the VQA system such as pos-tag attention, structed triplet learning and triplet attention is very general and can be inserted into almost any visual and language task
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Bottom Up Attention | 979 | 3 years ago | 56 | mit | Jupyter Notebook | |||||
| Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome | ||||||||||
| Bottom Up Attention Vqa | 606 | 5 years ago | 15 | gpl-3.0 | Python | |||||
| An efficient PyTorch implementation of the winning entry of the 2017 VQA Challenge. | ||||||||||
| Ban Vqa | 527 | 6 months ago | mit | Python | ||||||
| Bilinear attention networks for visual question answering | ||||||||||
| Mac Network | 445 | 3 years ago | 9 | apache-2.0 | Python | |||||
| Implementation for the paper "Compositional Attention Networks for Machine Reasoning" (Hudson and Manning, ICLR 2018) | ||||||||||
| Hiecoattenvqa | 284 | 6 years ago | 17 | Jupyter Notebook | ||||||
| Vqa2.0 Recent Approachs 2018.pytorch | 260 | 2 years ago | 4 | gpl-3.0 | Python | |||||
| A pytroch reimplementation of "Bilinear Attention Network", "Intra- and Inter-modality Attention", "Learning Conditioned Graph Structures", "Learning to count object", "Bottom-up top-down" for Visual Question Answering 2.0 | ||||||||||
| Mcan Vqa | 181 | 4 years ago | 2 | apache-2.0 | Python | |||||
| Deep Modular Co-Attention Networks for Visual Question Answering | ||||||||||
| Vqa Counting | 162 | 5 years ago | mit | Python | ||||||
| [ICLR 2018] Learning to Count Objects in Natural Images for Visual Question Answering | ||||||||||
| Tgif Qa | 139 | 3 years ago | Python | |||||||
| Repository for our CVPR 2017 and IJCV: TGIF-QA | ||||||||||
| Vqa_regat | 130 | 3 years ago | 4 | mit | Python | |||||
| Research Code for ICCV 2019 paper "Relation-aware Graph Attention Network for Visual Question Answering" | ||||||||||
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