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 611–620 of 2167 papers

Title	Date	Tasks	Status	Hype
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models	Mar 15, 2024	Few-Shot Image Classificationimage-classification	—Unverified	0
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering	Mar 14, 2024	Optical Character RecognitionOptical Character Recognition (OCR)	CodeCode Available	0
UniCode: Learning a Unified Codebook for Multimodal Large Language Models	Mar 14, 2024	QuantizationVisual Question Answering (VQA)	—Unverified	0
Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models	Mar 12, 2024	Concept AlignmentInstruction Following	CodeCode Available	1
Multi-modal Auto-regressive Modeling via Visual Words	Mar 12, 2024	Visual Question AnsweringVisual Question Answering (VQA)	CodeCode Available	1
OmniCount: Multi-label Object Counting with Semantic-Geometric Priors	Mar 8, 2024	ObjectObject Counting	—Unverified	0
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM	Mar 7, 2024	Question AnsweringRetrieval	—Unverified	0
TextMonkey: An OCR-Free Large Multimodal Model for Understanding Document	Mar 7, 2024	document understandingKey Information Extraction	CodeCode Available	5
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments	Mar 5, 2024	Language ModellingLarge Language Model	—Unverified	0
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation	Mar 5, 2024	Data AugmentationMedical Visual Question Answering	—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	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