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 1381–1390 of 2167 papers

Title	Date	Tasks	Status
Deep Exemplar Networks for VQA and VQG	Dec 19, 2019	DecoderQuestion Answering	—Unverified
Deep learning evaluation using deep linguistic processing	Jun 5, 2017	Deep LearningMultimodal Deep Learning	—Unverified
Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study	May 7, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
Deep Video Quality Assessor: From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network	Sep 1, 2018	SensitivityVideo Quality Assessment	—Unverified
RankDVQA: Deep VQA based on Ranking-inspired Hybrid Training	Feb 17, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
DePlot: One-shot visual language reasoning by plot-to-table translation	Dec 20, 2022	Chart Question AnsweringFactual Inconsistency Detection in Chart Captioning	—Unverified
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs	Apr 1, 2024	Common Sense ReasoningObject	—Unverified
Detect, Describe, Discriminate: Moving Beyond VQA for MLLM Evaluation	Sep 23, 2024	Multiple-choiceQuestion Answering	—Unverified
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions	May 18, 2024	Visual Question Answering (VQA)	—Unverified
DIEM: Decomposition-Integration Enhancing Multimodal Insights	Jan 1, 2024	MM-VetQuestion Answering	—Unverified

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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