MovieQA_CVPR2016
Movie QA system
This project explores question-answering in movies using various machine learning approaches.
Contains approaches introduced in the MovieQA benchmark dataset paper
80 stars
5 watching
31 forks
Language: Python
last commit: almost 8 years ago Related projects:
Repository | Description | Stars |
---|---|---|
simmerchan/kg-demo-for-movie | A knowledge graph-based question answering system for movies | 1,283 |
maluuba/newsqa | Compiles and provides structured access to Maluuba's NewsQA dataset for natural language question answering research. | 253 |
lupantech/scienceqa | Develops a framework for multimodal reasoning and question answering in science and other domains using natural language processing and machine learning techniques. | 606 |
microsoft/pica | An empirical study on using GPT-3 for multimodal question answering tasks with few-shot learning. | 84 |
milvlg/prophet | An implementation of a two-stage framework designed to prompt large language models with answer heuristics for knowledge-based visual question answering tasks. | 267 |
hwchase17/notion-qa | A Python-based question answering system built on top of Notion's database and OpenAI's API for natural language processing. | 2,139 |
ailab-cvc/seed-bench | A benchmark for evaluating large language models' ability to process multimodal input | 315 |
jayleicn/tvqa | PyTorch implementation of video question answering system based on TVQA dataset | 172 |
markdtw/vqa-winner-cvprw-2017 | Implementations and tools for training and fine-tuning a visual question answering model based on the 2017 CVPR workshop winner's approach. | 164 |
mohamedadaly/labr | A dataset of Arabic book reviews for natural language processing tasks | 44 |
mlpc-ucsd/bliva | A multimodal LLM designed to handle text-rich visual questions | 269 |
noagarcia/roll-videoqa | A PyTorch-based model for answering questions about videos based on unseen scenes and storylines | 19 |
cadene/vqa.pytorch | A PyTorch implementation of visual question answering with multimodal representation learning | 716 |
jnhwkim/nips-mrn-vqa | This project presents a neural network model designed to answer visual questions by combining question and image features in a residual learning framework. | 39 |
cmmmu-benchmark/cmmmu | An evaluation benchmark and dataset for multimodal question answering models | 46 |