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 1181–1190 of 2167 papers

Title	Date	Tasks	Status
A Framework to Map VMAF with the Probability of Just Noticeable Difference between Video Encoding Recipes	May 16, 2022	Video Quality AssessmentVisual Question Answering (VQA)	—Unverified
A Free Lunch in Generating Datasets: Building a VQG and VQA System with Attention and Humans in the Loop	Nov 30, 2019	Question AnsweringQuestion Generation	—Unverified
A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions	Mar 26, 2024	Gaze Target EstimationQuestion Answering	—Unverified
AGFSync: Leveraging AI-Generated Feedback for Preference Optimization in Text-to-Image Generation	Mar 20, 2024	Image GenerationText to Image Generation	—Unverified
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP	Oct 31, 2024	Image CaptioningPrompt Learning	—Unverified
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models	Nov 16, 2021	Language ModelingLanguage Modelling	—Unverified
AI2D-RST: A multimodal corpus of 1000 primary school science diagrams	Dec 9, 2019	Question AnsweringVisual Question Answering	—Unverified
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering	Jul 1, 2020	Question AnsweringVisual Question Answering	—Unverified
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks	Apr 2, 2017	Multi-Task LearningQuestion Answering	—Unverified
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models	Nov 8, 2024	QuantizationQuestion Answering	—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