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 1701–1710 of 2167 papers

Title	Date	Tasks	Status	Hype
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks	Apr 13, 2020	Cross-Modal RetrievalImage Captioning	CodeCode Available	2
An Entropy Clustering Approach for Assessing Visual Question Difficulty	Apr 12, 2020	ClusteringQuestion Answering	CodeCode Available	0
YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos	Apr 12, 2020	Action UnderstandingQuestion Answering	CodeCode Available	1
Visual Grounding Methods for VQA are Working for the Wrong Reasons!	Apr 12, 2020	Question AnsweringVisual Grounding	CodeCode Available	1
Rephrasing visual questions by specifying the entropy of the answer distribution	Apr 10, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing	Apr 8, 2020	DiversityQuestion Answering	—Unverified	0
Evaluating Multimodal Representations on Visual Semantic Textual Similarity	Apr 4, 2020	BenchmarkingImage Captioning	CodeCode Available	1
Generating Rationales in Visual Question Answering	Apr 4, 2020	Question AnsweringVisual Question Answering	—Unverified	0
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers	Apr 2, 2020	Image-text matchingImage-text Retrieval	CodeCode Available	1
X-Linear Attention Networks for Image Captioning	Mar 31, 2020	DecoderFine-Grained Visual Recognition	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