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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 151–160 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone	Jun 15, 2022	Described Object DetectionImage Captioning	CodeCode Available	1	5
Coarse-to-Fine Reasoning for Visual Question Answering	Oct 6, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1	5
COBRA: Contrastive Bi-Modal Representation Algorithm	May 7, 2020	Cross-Modal RetrievalImage Captioning	CodeCode Available	1	5
FiLM: Visual Reasoning with a General Conditioning Layer	Sep 22, 2017	Image Retrieval with Multi-Modal QueryVisual Question Answering (VQA)	CodeCode Available	1	5
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning	Jun 11, 2020	Question AnsweringReinforcement Learning (RL)	CodeCode Available	1	5
Faithful Multimodal Explanation for Visual Question Answering	Sep 8, 2018	Explanatory Visual Question AnsweringQuestion Answering	CodeCode Available	1	5
Clover: Towards A Unified Video-Language Alignment and Fusion Model	Jul 16, 2022	Language ModelingLanguage Modelling	CodeCode Available	1	5
An Empirical Study of Training End-to-End Vision-and-Language Transformers	Nov 3, 2021	Cross-Modal RetrievalDecoder	CodeCode Available	1	5
CoCa: Contrastive Captioners are Image-Text Foundation Models	May 4, 2022	Action ClassificationDecoder	CodeCode Available	1	5
Fast Prompt Alignment for Text-to-Image Generation	Dec 11, 2024	Image GenerationIn-Context Learning	CodeCode Available	1	5

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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