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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 371–380 of 2167 papers

Title	Date	Tasks	Status	Hype
ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation	Oct 11, 2024	DiagnosticLanguage Modeling	—Unverified	0
Secure Video Quality Assessment Resisting Adversarial Attacks	Oct 9, 2024	Adversarial DefenseVideo Quality Assessment	—Unverified	0
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning	Oct 8, 2024	Image RetrievalMath	—Unverified	0
ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments	Oct 8, 2024	DecoderQuestion Answering	CodeCode Available	0
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback	Oct 8, 2024	MathSequential Decision Making	CodeCode Available	1
ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models	Oct 7, 2024	Question AnsweringVisual Question Answering	CodeCode Available	1
MC-CoT: A Modular Collaborative CoT Framework for Zero-shot Medical-VQA with LLM and MLLM Integration	Oct 6, 2024	Medical Visual Question AnsweringQuestion Answering	CodeCode Available	1
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions	Oct 5, 2024	BenchmarkingHallucination	CodeCode Available	0
BadCM: Invisible Backdoor Attack Against Cross-Modal Learning	Oct 3, 2024	Backdoor AttackCross-Modal Retrieval	CodeCode Available	1
Video Instruction Tuning With Synthetic Data	Oct 3, 2024	3D Question Answering (3D-QA)	—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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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