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 581–590 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Notes-guided MLLM Reasoning: Enhancing MLLM with Knowledge and Visual Notes for Visual Question Answering	Jan 1, 2025	Large Language ModelMultimodal Large Language Model	CodeCode Available	1	5
PitVQA: Image-grounded Text Embedding LLM for Visual Question Answering in Pituitary Surgery	May 22, 2024	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models	Mar 10, 2023	Language ModelingLanguage Modelling	CodeCode Available	1	5
PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models	May 23, 2022	Language ModelingLanguage Modelling	CodeCode Available	1	5
Hierarchical multimodal transformers for Multi-Page DocVQA	Dec 7, 2022	DecoderQuestion Answering	CodeCode Available	1	5
Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering	Sep 19, 2024	HallucinationHallucination Evaluation	CodeCode Available	1	5
Evaluating Multimodal Representations on Visual Semantic Textual Similarity	Apr 4, 2020	BenchmarkingImage Captioning	CodeCode Available	1	5
Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies	Oct 21, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Think Locally, Act Globally: Federated Learning with Local and Global Representations	Jan 6, 2020	Federated LearningRepresentation Learning	CodeCode Available	1	5
Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment	Apr 13, 2023	Video Quality AssessmentVisual Question Answering (VQA)	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