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 211–220 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
ConceptBert: Concept-Aware Representation for Visual Question Answering	Nov 1, 2020	Common Sense ReasoningQuestion Answering	CodeCode Available	1	5
CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions	Dec 8, 2020	counterfactualDescriptive	CodeCode Available	1	5
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of Experts	Nov 16, 2024	Mixture-of-ExpertsOptical Character Recognition (OCR)	CodeCode Available	1	5
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers	May 27, 2023	Image CaptioningImage Retrieval	CodeCode Available	1	5
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering	Jul 26, 2022	Causal InferenceQuestion Answering	CodeCode Available	1	5
Hierarchical multimodal transformers for Multi-Page DocVQA	Dec 7, 2022	DecoderQuestion Answering	CodeCode Available	1	5
BadCM: Invisible Backdoor Attack Against Cross-Modal Learning	Oct 3, 2024	Backdoor AttackCross-Modal Retrieval	CodeCode Available	1	5
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs	Nov 27, 2023	Adversarial RobustnessVisual Question Answering (VQA)	CodeCode Available	1	5
Cross-Modality Relevance for Reasoning on Language and Vision	May 12, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1	5
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning	Oct 25, 2021	Arithmetic ReasoningMathematical Question Answering	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