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 1431–1440 of 2167 papers

Title	Date	Tasks	Status
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering	Dec 30, 2024	Image CaptioningObject Recognition	—Unverified
Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approach	May 1, 2024	Computational EfficiencyQuestion Answering	—Unverified
Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations	Jan 8, 2025	Visual Question Answering (VQA)	—Unverified
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation	Mar 5, 2024	Data AugmentationMedical Visual Question Answering	—Unverified
Enhancing Instruction-Following Capability of Visual-Language Models by Reducing Image Redundancy	Nov 23, 2024	Instruction FollowingMME	—Unverified
Enhancing Multi-hop Reasoning in Vision-Language Models via Self-Distillation with Multi-Prompt Ensembling	Mar 3, 2025	Answer GenerationComputational Efficiency	—Unverified
Enhancing Multi-Image Question Answering via Submodular Subset Selection	May 15, 2025	Question AnsweringRetrieval	—Unverified
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference	Nov 21, 2022	Natural Language InferenceQuestion Answering	—Unverified
Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion	Aug 14, 2024	Question AnsweringVisual Question Answering	—Unverified
Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering	Oct 18, 2022	Passage RetrievalQuestion 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