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 551–575 of 2167 papers

Title	Date	Tasks	Status	Hype
YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos	Apr 12, 2020	Action UnderstandingQuestion Answering	CodeCode Available	1
Evaluating Multimodal Representations on Visual Semantic Textual Similarity	Apr 4, 2020	BenchmarkingImage Captioning	CodeCode Available	1
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
Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text	Mar 31, 2020	Graph Neural NetworkQuestion Answering	CodeCode Available	1
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI	Mar 16, 2020	BenchmarkingExplainable Artificial Intelligence (XAI)	CodeCode Available	1
Counterfactual Samples Synthesizing for Robust Visual Question Answering	Mar 14, 2020	counterfactualQuestion Answering	CodeCode Available	1
PathVQA: 30000+ Questions for Medical Visual Question Answering	Mar 7, 2020	AI AgentMedical Visual Question Answering	CodeCode Available	1
Visual Commonsense R-CNN	Feb 27, 2020	Image CaptioningRepresentation Learning	CodeCode Available	1
Hierarchical Conditional Relation Networks for Video Question Answering	Feb 25, 2020	Audio-Visual Question Answering (AVQA)Question Answering	CodeCode Available	1
Multimodal fusion of imaging and genomics for lung cancer recurrence prediction	Feb 5, 2020	Computed Tomography (CT)Question Answering	CodeCode Available	1
Break It Down: A Question Understanding Benchmark	Jan 31, 2020	Open-Domain Question AnsweringQuestion Answering	CodeCode Available	1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features	Jan 14, 2020	ClassificationDiversity	CodeCode Available	1
In Defense of Grid Features for Visual Question Answering	Jan 10, 2020	Image CaptioningQuestion Answering	CodeCode Available	1
Think Locally, Act Globally: Federated Learning with Local and Global Representations	Jan 6, 2020	Federated LearningRepresentation Learning	CodeCode Available	1
Overcoming Data Limitation in Medical Visual Question Answering	Sep 26, 2019	DenoisingMedical Visual Question Answering	CodeCode Available	1
UNITER: UNiversal Image-TExt Representation Learning	Sep 25, 2019	Image-text matchingImage-text Retrieval	CodeCode Available	1
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases	Sep 9, 2019	Natural Language InferenceQuestion Answering	CodeCode Available	1
VL-BERT: Pre-training of Generic Visual-Linguistic Representations	Aug 22, 2019	Image-text matchingLanguage Modelling	CodeCode Available	1
LXMERT: Learning Cross-Modality Encoder Representations from Transformers	Aug 20, 2019	Language ModelingLanguage Modelling	CodeCode Available	1
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering	Aug 14, 2019	Embodied Question AnsweringQuestion Answering	CodeCode Available	1
VisualBERT: A Simple and Performant Baseline for Vision and Language	Aug 9, 2019	Language ModelingLanguage Modelling	CodeCode Available	1
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks	Aug 6, 2019	Image RetrievalQuestion Answering	CodeCode Available	1
Scene Text Visual Question Answering	May 31, 2019	Question AnsweringVisual Question Answering	CodeCode Available	1
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge	May 31, 2019	object-detectionObject Detection	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