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 651–660 of 2167 papers

Title	Date	Tasks	Status	Hype
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations	Jan 31, 2024	Question AnsweringVisual Question Answering (VQA)	—Unverified	0
Common Sense Reasoning for Deepfake Detection	Jan 31, 2024	Binary ClassificationCommon Sense Reasoning	CodeCode Available	3
Proximity QA: Unleashing the Power of Multi-Modal Large Language Models for Spatial Proximity Analysis	Jan 31, 2024	Multi-Task LearningQuestion Answering	CodeCode Available	0
Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA	Jan 29, 2024	BenchmarkingImage Comprehension	—Unverified	0
LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering	Jan 29, 2024	Language ModelingLanguage Modelling	—Unverified	0
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning	Jan 28, 2024	Data AugmentationQuestion Answering	—Unverified	0
Free Form Medical Visual Question Answering in Radiology	Jan 23, 2024	DiagnosticForm	—Unverified	0
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities	Jan 22, 2024	Question AnsweringSpatial Reasoning	—Unverified	0
Q&A Prompts: Discovering Rich Visual Clues through Mining Question-Answer Prompts for VQA requiring Diverse World Knowledge	Jan 19, 2024	Question AnsweringQuestion Generation	CodeCode Available	1
Veagle: Advancements in Multimodal Representation Learning	Jan 18, 2024	Image CaptioningLanguage Modelling	CodeCode Available	1

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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