Visual Question Answering
680 papers with code • 20 benchmarks • 21 datasets
Benchmarks
These leaderboards are used to track progress in Visual Question Answering
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Libraries
Use these libraries to find Visual Question Answering models and implementationsDatasets
MS COCO
GQA
Visual Question Answering v2.0
TextVQA
VizWiz
MMBench
MM-Vet
MSRVTT-QA
MathVista
Most implemented papers
Hierarchical Question-Image Co-Attention for Visual Question Answering
jiasenlu/HieCoAttenVQA
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NeurIPS 2016
In addition, our model reasons about the question (and consequently the image via the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN).
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
airsplay/lxmert
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IJCNLP 2019
In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder.
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Visual Instruction Tuning
haotian-liu/LLaVA
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NeurIPS 2023
Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field.
Hadamard Product for Low-rank Bilinear Pooling
Bilinear models provide rich representations compared with linear models.
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
peteanderson80/Matterport3DSimulator
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CVPR 2018
This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering.
Bilinear Attention Networks
jnhwkim/ban-vqa
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NeurIPS 2018
In this paper, we propose bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly.
Simple Baseline for Visual Question Answering
We describe a very simple bag-of-words baseline for visual question answering.
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
We propose to counter these language priors for the task of Visual Question Answering (VQA) and make vision (the V in VQA) matter!
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
sea-snell/implicit-language-q-learning
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ICCV 2017
Specifically, we pose a cooperative 'image guessing' game between two agents -- Qbot and Abot -- who communicate in natural language dialog so that Qbot can select an unseen image from a lineup of images.
