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 561–570 of 2167 papers

Title	Date	Tasks	Status	Hype
Evaluating Multimodal Representations on Visual Semantic Textual Similarity	Apr 4, 2020	BenchmarkingImage Captioning	CodeCode Available	1
Learning to Answer Visual Questions from Web Videos	May 10, 2022	Dataset GenerationQuestion Answering	CodeCode Available	1
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding	Oct 12, 2022	document-image-classificationDocument Image Classification	CodeCode Available	1
Learning to Discretely Compose Reasoning Module Networks for Video Captioning	Jul 17, 2020	DecoderQuestion Answering	CodeCode Available	1
Less is More: A Simple yet Effective Token Reduction Method for Efficient Multi-modal LLMs	Sep 17, 2024	Question AnsweringToken Reduction	CodeCode Available	1
Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling	Feb 11, 2021	Question AnsweringRetrieval	CodeCode Available	1
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion	Feb 26, 2023	Video Quality AssessmentVisual Question Answering (VQA)	CodeCode Available	1
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner	May 19, 2023	Dense CaptioningImage Captioning	CodeCode Available	1
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?	Feb 23, 2023	Open-Domain Question AnsweringQuestion Answering	CodeCode Available	1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization	Oct 7, 2016	General ClassificationImage Attribution	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