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 231–240 of 2167 papers

Title	Date	Tasks	Status	Hype
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation	Jan 6, 2025	Language Model EvaluationLanguage Modeling	CodeCode Available	1
Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment	Jan 6, 2025	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified	0
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?	Jan 5, 2025	Image CaptioningImage to text	CodeCode Available	1
Guiding Medical Vision-Language Models with Explicit Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations	Jan 4, 2025	DecoderVisual Question Answering (VQA)	—Unverified	0
Accounting for Focus Ambiguity in Visual Questions	Jan 4, 2025	Question AnsweringVisual Question Answering	—Unverified	0
Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models	Jan 3, 2025	Binary ClassificationFace Anti-Spoofing	—Unverified	0
MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning	Jan 3, 2025	DiagnosticGeneral Knowledge	—Unverified	0
CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering	Jan 2, 2025	Multiple-choiceQuestion Answering	—Unverified	0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts	Jan 1, 2025	Contrastive LearningText Retrieval	—Unverified	0
F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics	Jan 1, 2025	DiversityFederated Learning	—Unverified	0

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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