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 1561–1570 of 2167 papers

Title	Date	Tasks	Status	Hype
Trying Bilinear Pooling in Video-QA	Dec 18, 2020	Question AnsweringVideo Question Answering	—Unverified	0
On Modality Bias in the TVQA Dataset	Dec 18, 2020	Question AnsweringVideo Question Answering	CodeCode Available	0
Overcoming Language Priors with Self-supervised Learning for Visual Question Answering	Dec 17, 2020	Question AnsweringSelf-Supervised Learning	CodeCode Available	1
Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding	Dec 14, 2020	Question AnsweringVisual Question Answering	CodeCode Available	1
KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning	Dec 13, 2020	SentenceVisual Commonsense Reasoning	—Unverified	0
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps	Dec 9, 2020	DecoderImage Captioning	—Unverified	0
Study on the Assessment of the Quality of Experience of Streaming Video	Dec 8, 2020	regressionVideo Quality Assessment	CodeCode Available	0
TAP: Text-Aware Pre-training for Text-VQA and Text-Caption	Dec 8, 2020	Caption GenerationLanguage Modeling	CodeCode Available	1
CRAFT: A Benchmark for Causal Reasoning About Forces and inTeractions	Dec 8, 2020	counterfactualDescriptive	CodeCode Available	1
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding	Dec 5, 2020	image-classificationImage Classification	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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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