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 661–670 of 2167 papers

Title	Date	Tasks	Status	Hype
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation	Jan 18, 2024	Caption GenerationLanguage Modeling	—Unverified	0
Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation	Jan 18, 2024	Contrastive LearningPrompt Engineering	CodeCode Available	1
COCO is "ALL'' You Need for Visual Instruction Fine-tuning	Jan 17, 2024	AllImage Captioning	—Unverified	0
Video Quality Assessment Based on Swin TransformerV2 and Coarse to Fine Strategy	Jan 16, 2024	Image Quality AssessmentVideo Quality Assessment	—Unverified	0
Uncovering the Full Potential of Visual Grounding Methods in VQA	Jan 15, 2024	Question AnsweringVisual Grounding	CodeCode Available	0
BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining	Jan 12, 2024	Question AnsweringVisual Question Answering	—Unverified	0
Generalizing Visual Question Answering from Synthetic to Human-Written Questions via a Chain of QA with a Large Language Model	Jan 12, 2024	Language ModelingLanguage Modelling	CodeCode Available	0
Cross-modal Retrieval for Knowledge-based Visual Question Answering	Jan 11, 2024	Cross-Modal RetrievalQuestion Answering	CodeCode Available	1
Hallucination Benchmark in Medical Visual Question Answering	Jan 11, 2024	HallucinationMedical Visual Question Answering	CodeCode Available	0
MISS: A Generative Pretraining and Finetuning Approach for Med-VQA	Jan 10, 2024	Medical Visual Question AnsweringMulti-Task Learning	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