aioz-ai/CFR_VQA
Coarse-to-Fine Reasoning for Visual Question Answering (CVPRW'22)
This project helps systems understand and answer questions about images. You provide an image and a natural language question (e.g., "What color is the car?"), and it outputs an accurate answer to that question. It is intended for AI researchers and developers working on advanced image understanding and human-computer interaction.
No commits in the last 6 months.
Use this if you are developing or researching Visual Question Answering (VQA) systems and need a robust framework to bridge the gap between visual information and semantic questions.
Not ideal if you are looking for an off-the-shelf, plug-and-play solution for non-developers, or if your primary task is simple image labeling or object detection without complex reasoning.
Stars
49
Forks
13
Language
Python
License
MIT
Category
Last pushed
Nov 03, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aioz-ai/CFR_VQA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
open-mmlab/mmpretrain
OpenMMLab Pre-training Toolbox and Benchmark
facebookresearch/mmf
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)
adambielski/siamese-triplet
Siamese and triplet networks with online pair/triplet mining in PyTorch
HuaizhengZhang/Awsome-Deep-Learning-for-Video-Analysis
Papers, code and datasets about deep learning and multi-modal learning for video analysis
KaiyangZhou/pytorch-vsumm-reinforce
Unsupervised video summarization with deep reinforcement learning (AAAI'18)