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 451–475 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models	Nov 9, 2024	object-detectionObject Detection	CodeCode Available	1	5
Kosmos-2: Grounding Multimodal Large Language Models to the World	Jun 26, 2023	Image CaptioningIn-Context Learning	CodeCode Available	1	5
Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment	Aug 29, 2022	cross-modal alignmentImage-text Retrieval	CodeCode Available	1	5
Learning to Discretely Compose Reasoning Module Networks for Video Captioning	Jul 17, 2020	DecoderQuestion Answering	CodeCode Available	1	5
3D-Aware Visual Question Answering about Parts, Poses and Occlusions	Oct 27, 2023	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Deep Multimodal Neural Architecture Search	Apr 25, 2020	DecoderImage-text matching	CodeCode Available	1	5
MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale	Dec 6, 2024	Multimodal ReasoningVisual Question Answering	CodeCode Available	1	5
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors	Oct 18, 2021	Descriptivenamed-entity-recognition	CodeCode Available	1	5
Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset	Nov 5, 2024	BenchmarkingLanguage Modeling	CodeCode Available	1	5
Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts	Oct 31, 2023	Image CaptioningLanguage Modeling	CodeCode Available	1	5
Language-Informed Visual Concept Learning	Dec 6, 2023	DisentanglementNovel Concepts	CodeCode Available	1	5
Describe Anything Model for Visual Question Answering on Text-rich Images	Jul 16, 2025	DescriptiveLanguage Modeling	CodeCode Available	1	5
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering	Nov 13, 2023	Decision MakingExplanation Generation	CodeCode Available	1	5
Large-Scale Adversarial Training for Vision-and-Language Representation Learning	Jun 11, 2020	Image-text RetrievalQuestion Answering	CodeCode Available	1	5
MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting	Oct 13, 2022	Image CaptioningQuestion Answering	CodeCode Available	1	5
Detecting and Preventing Hallucinations in Large Vision Language Models	Aug 11, 2023	16kHallucination	CodeCode Available	1	5
Detecting Hate Speech in Multi-modal Memes	Dec 29, 2020	Binary ClassificationHate Speech Detection	CodeCode Available	1	5
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling	Sep 4, 2022	Fill MaskOptical Flow Estimation	CodeCode Available	1	5
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding	Apr 26, 2021	Generalized Referring Expression ComprehensionPhrase Grounding	CodeCode Available	1	5
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images	Oct 1, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1	5
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA	Sep 10, 2021	Image CaptioningQuestion Answering	CodeCode Available	1	5
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering	Jun 1, 2023	Optical Character Recognition (OCR)Question Answering	CodeCode Available	1	5
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models	Mar 12, 2024	Concept AlignmentInstruction Following	CodeCode Available	1	5
LaTr: Layout-Aware Transformer for Scene-Text VQA	Dec 23, 2021	Optical Character Recognition (OCR)Question Answering	CodeCode Available	1	5
LXMERT: Learning Cross-Modality Encoder Representations from Transformers	Aug 20, 2019	Language ModelingLanguage Modelling	CodeCode Available	1	5

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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