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 1381–1390 of 2167 papers

Title	Date	Tasks	Status
Scene Understanding Enabled Semantic Communication with Open Channel Coding	Jan 24, 2025	Question AnsweringScene Understanding	—Unverified
Scientists' First Exam: Probing Cognitive Abilities of MLLM via Perception, Understanding, and Reasoning	Jun 12, 2025	AttributeMultimodal Reasoning	—Unverified
SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answering	Apr 4, 2023	counterfactualMetric Learning	—Unverified
Secure Video Quality Assessment Resisting Adversarial Attacks	Oct 9, 2024	Adversarial DefenseVideo Quality Assessment	—Unverified
SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors	Mar 18, 2024	HallucinationMotion Planning	—Unverified
Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework	Mar 11, 2025	Conformal PredictionMultimodal Reasoning	—Unverified
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings	Dec 31, 2020	Common Sense ReasoningKnowledge Graph Embeddings	—Unverified
SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering	Oct 1, 2019	Embodied Question AnsweringQuestion Answering	—Unverified
Segmentation-guided Attention for Visual Question Answering from Remote Sensing Images	Jul 11, 2024	Question AnsweringSegmentation	—Unverified
Segmentation Guided Attention Networks for Visual Question Answering	Jul 1, 2017	Common Sense ReasoningQuestion Answering	—Unverified

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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