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 481–490 of 2167 papers

Title	Date	Tasks	Status	Hype
A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding	Jul 2, 2024	document understandingKey Information Extraction	CodeCode Available	2
Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness	Jul 2, 2024	Image CaptioningQuestion Answering	—Unverified	0
https://arxiv.org/abs/2407.00634	Jul 2, 2024	Video CaptioningVideo Description	CodeCode Available	0
μ-Bench: A Vision-Language Benchmark for Microscopy Understanding	Jul 1, 2024	Cell DetectionClassification	CodeCode Available	0
Hierarchical Memory for Long Video QA	Jun 30, 2024	GPUQuestion Answering	—Unverified	0
Tarsier: Recipes for Training and Evaluating Large Video Description Models	Jun 30, 2024	Video CaptioningVideo Description	CodeCode Available	4
SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs	Jun 28, 2024	RAGRetrieval-augmented Generation	—Unverified	0
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis	Jun 28, 2024	Visual Question Answering (VQA)Visual Reasoning	CodeCode Available	1
STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering	Jun 28, 2024	Medical DiagnosisMedical Question Answering	CodeCode Available	1
Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature Distillation	Jun 27, 2024	Continual LearningQuestion Answering	CodeCode Available	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