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 1111–1120 of 2167 papers

Title	Date	Tasks	Status
M^2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge Base	Dec 16, 2023	cross-modal alignmentKnowledge Graphs	CodeCode Available
Advancing Surgical VQA with Scene Graph Knowledge	Dec 15, 2023	Question AnsweringVisual Question Answering	—Unverified
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment	Dec 14, 2023	Knowledge DistillationModel Compression	—Unverified
BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering	Dec 13, 2023	Medical Visual Question AnsweringQuestion Answering	—Unverified
Causal-CoG: A Causal-Effect Look at Context Generation for Boosting Multi-modal Language Models	Dec 9, 2023	Question AnsweringVisual Question Answering	—Unverified
Lyrics: Boosting Fine-grained Language-Vision Alignment and Comprehension via Semantic-aware Visual Objects	Dec 8, 2023	Image Captioningobject-detection	—Unverified
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks	Dec 6, 2023	Image Captioningimage-classification	—Unverified
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models	Dec 5, 2023	Language ModelingLanguage Modelling	—Unverified
MedXChat: A Unified Multimodal Large Language Model Framework towards CXRs Understanding and Generation	Dec 4, 2023	Instruction FollowingLanguage Modeling	—Unverified
Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario	Dec 4, 2023	Language ModelingLanguage Modelling	—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