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 1541–1550 of 2167 papers

Title	Date	Tasks	Status
Understanding the Role of Scene Graphs in Visual Question Answering	Jan 14, 2021	Graph GenerationQuestion Answering	—Unverified
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering	Dec 21, 2022	Data AugmentationDecision Making	—Unverified
UniCode: Learning a Unified Codebook for Multimodal Large Language Models	Mar 14, 2024	QuantizationVisual Question Answering (VQA)	—Unverified
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training	Jan 11, 2022	DecoderImage Captioning	—Unverified
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action	Jan 1, 2024	Image GenerationInstruction Following	—Unverified
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks	Jun 17, 2022	Depth EstimationImage Generation	—Unverified
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation	Dec 10, 2021	Image-text matchingImage-text Retrieval	—Unverified
Unified Scene Representation and Reconstruction for 3D Large Language Models	Apr 19, 2024	3D ReconstructionScene Understanding	—Unverified
Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training	Nov 20, 2024	Contrastive Learningimage-classification	—Unverified
UniRVQA: A Unified Framework for Retrieval-Augmented Vision Question Answering via Self-Reflective Joint Training	Apr 5, 2025	ArticlesQuestion 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