A simple attention deep learning model to answer questions about a given video with the most relevant video intervals as answers.
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Updated
Jul 6, 2019 - Python
A simple attention deep learning model to answer questions about a given video with the most relevant video intervals as answers.
A simple attention deep learning model to answer questions about a given video with the most relevant video intervals as answers.
PyTorch code for ROLL, a knowledge-based video story question answering model.
ROCK model for Knowledge-Based VQA in Videos
Data and PyTorch code for the LifeQA LREC 2020 paper.
Given a video, we are able to automaticaly answer questions about what is happening in the video.
[ICCV 2021] On the hidden treasure of dialog in video question answering
[CVPR 2022] A large-scale public benchmark dataset for video question-answering, especially about evidence and commonsense reasoning. The code used in our paper "From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering", CVPR2022.
Video as Conditional Graph Hierarchy for Multi-Granular Question Answering (AAAI'22, Oral)
Align and Prompt: Video-and-Language Pre-training with Entity Prompts
DramaQA Starter Code (2021)
[ACL 2020] PyTorch code for TVQA+: Spatio-Temporal Grounding for Video Question Answering
Multi-Scale Progressive Attention Network for Video Question Answering
LifeQA website code
This repo contains code for Invariant Grounding for Video Question Answering
WildQA website code
Video Graph Transformer for Video Question Answering (ECCV'22)
Code for ACL SustaiNLP 2023 paper "Is a Video worth n × n Images? A Highly Efficient Approach to Transformer-based Video Question Answering"
Code for ACL SRW 2023 paepr "Semantic-aware Dynamic Retrospective-Prospective Reasoning for Event-level Video Question Answering"
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video (ICML 2023)
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