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 761–770 of 2167 papers

Title	Date	Tasks	Status	Hype
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation	Oct 27, 2023	Image GenerationQuestion Answering	—Unverified	0
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs	Oct 26, 2023	AttributeMachine Translation	CodeCode Available	0
Exploring Question Decomposition for Zero-Shot VQA	Oct 25, 2023	Question AnsweringVisual Question Answering	—Unverified	0
Towards Perceiving Small Visual Details in Zero-shot Visual Question Answering with Multimodal LLMs	Oct 24, 2023	Question AnsweringVisual Question Answering	CodeCode Available	1
Geometry-Aware Video Quality Assessment for Dynamic Digital Human	Oct 24, 2023	AttributeVideo Quality Assessment	—Unverified	0
Large Language Models are Temporal and Causal Reasoners for Video Question Answering	Oct 24, 2023	Natural Language UnderstandingQuestion Answering	CodeCode Available	1
LXMERT Model Compression for Visual Question Answering	Oct 23, 2023	modelModel Compression	CodeCode Available	0
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models	Oct 23, 2023	DiagnosticHallucination	CodeCode Available	2
A Simple Baseline for Knowledge-Based Visual Question Answering	Oct 20, 2023	In-Context LearningQuestion Answering	CodeCode Available	0
RSAdapter: Adapting Multimodal Models for Remote Sensing Visual Question Answering	Oct 19, 2023	Image CaptioningQuestion Answering	CodeCode Available	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	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