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 891–900 of 2167 papers

Title	Date	Tasks	Status
Secure Video Quality Assessment Resisting Adversarial Attacks	Oct 9, 2024	Adversarial DefenseVideo Quality Assessment	—Unverified
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning	Oct 8, 2024	Image RetrievalMath	—Unverified
ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments	Oct 8, 2024	DecoderQuestion Answering	CodeCode Available
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions	Oct 5, 2024	BenchmarkingHallucination	CodeCode Available
Video Instruction Tuning With Synthetic Data	Oct 3, 2024	3D Question Answering (3D-QA)	—Unverified
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model	Oct 3, 2024	image-classificationImage Classification	—Unverified
Backdooring Vision-Language Models with Out-Of-Distribution Data	Oct 2, 2024	Image CaptioningImage to text	—Unverified
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities	Oct 2, 2024	Question AnsweringVisual Question Answering	—Unverified
BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data	Oct 1, 2024	Code GenerationLogical Reasoning	CodeCode Available
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks	Oct 1, 2024	BenchmarkingFairness	—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