Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

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Showing 1026–1050 of 2167 papers

Title	Date	Tasks	Status	Score
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery	Feb 26, 2024	Continual LearningExemplar-Free	CodeCode Available	5
Dual Attention Networks for Multimodal Reasoning and Matching	Nov 2, 2016	Collaborative InferenceImage-text matching	CodeCode Available	5
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models	Aug 26, 2024	Large Language ModelVideo Quality Assessment	CodeCode Available	5
Looking Beyond Visible Cues: Implicit Video Question Answering via Dual-Clue Reasoning	Jun 9, 2025	Future predictionQuestion Answering	CodeCode Available	5
Mimic and Fool: A Task Agnostic Adversarial Attack	Jun 11, 2019	Adversarial AttackImage Captioning	CodeCode Available	5
Simple Baseline for Visual Question Answering	Dec 7, 2015	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	5
Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model	Jun 10, 2022	Question AnsweringTask 2	—Unverified	0
LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?	Mar 25, 2025	Autonomous NavigationQuestion Answering	—Unverified	0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision	Apr 20, 2020	counterfactualimage-classification	—Unverified	0
Learning Visual Knowledge Memory Networks for Visual Question Answering	Jun 13, 2018	Question AnsweringVisual Question Answering	—Unverified	0
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions	Jul 2, 2024	DiagnosticInstruction Following	—Unverified	0
An Evaluation of Image-Based Verb Prediction Models against Human Eye-Tracking Data	Jun 1, 2018	General ClassificationQuestion Answering	—Unverified	0
Learning to Specialize with Knowledge Distillation for Visual Question Answering	Dec 1, 2018	General ClassificationGeneral Knowledge	—Unverified	0
Learning to Select Question-Relevant Relations for Visual Question Answering	Jun 1, 2021	Graph AttentionQuestion Answering	—Unverified	0
Learning to Recognize the Unseen Visual Predicates	Sep 25, 2019	Question AnsweringVisual Question Answering	—Unverified	0
Neural Reasoning, Fast and Slow, for Video Question Answering	Jul 10, 2019	Natural QuestionsQuestion Answering	—Unverified	0
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment	Jul 1, 2023	Language ModelingLanguage Modelling	—Unverified	0
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios	Nov 20, 2024	Question AnsweringVisual Question Answering (VQA)	—Unverified	0
An Evaluation of GPT-4V and Gemini in Online VQA	Dec 17, 2023	Question AnsweringVisual Question Answering	—Unverified	0
Adventurer's Treasure Hunt: A Transparent System for Visually Grounded Compositional Visual Question Answering based on Scene Graphs	Jun 28, 2021	Question AnsweringTask 2	—Unverified	0
Learning to Disambiguate by Asking Discriminative Questions	Aug 9, 2017	BenchmarkingImage Captioning	—Unverified	0
Domain-robust VQA with diverse datasets and methods but no target labels	Mar 29, 2021	Domain AdaptationObject Recognition	—Unverified	0
Do Explanations make VQA Models more Predictable to a Human?	Oct 29, 2018	Question AnsweringVisual Question Answering	—Unverified	0
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering	Sep 11, 2024	Question AnsweringVisual Question Answering	—Unverified	0
Learning to Compose Diversified Prompts for Image Emotion Classification	Jan 26, 2022	ClassificationEmotion Classification	—Unverified	0

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All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified