https://github.com/amanchadha/iperceive

Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Python3 | PyTorch | CNNs | Causality | Reasoning | LSTMs | Transformers | Multi-Head Self Attention | Published in IEEE Winter Conference on Applications of Computer Vision (WACV) 2021
https://github.com/amanchadha/iperceive

attention captioning captioning-videos causality common-sense convolutional-neural-networks dense-captioning distilling-the-knowledge lstm multi-modal python python3 pytorch question-answering reasoning resnets self-attention transformers video videoqa

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Applying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Python3 | PyTorch | CNNs | Causality | Reasoning | LSTMs | Transformers | Multi-Head Self Attention | Published in IEEE Winter Conference on Applications of Computer Vision (WACV) 2021

• Host: GitHub
• URL: https://github.com/amanchadha/iperceive
• Owner: amanchadha
• License: mit
• Created: 2020-06-10T06:13:34.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-06-22T02:16:43.000Z (over 2 years ago)
• Last Synced: 2023-06-01T06:09:46.631Z (over 1 year ago)
• Topics: attention, captioning, captioning-videos, causality, common-sense, convolutional-neural-networks, dense-captioning, distilling-the-knowledge, lstm, multi-modal, python, python3, pytorch, question-answering, reasoning, resnets, self-attention, transformers, video, videoqa
• Language: Python
• Homepage: https://arxiv.org/abs/2011.07735
• Size: 4.27 MB
• Stars: 66
• Watchers: 6
• Forks: 19
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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