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 151–175 of 2167 papers

Title	Date	Tasks	Status	Hype	Score
Combo of Thinking and Observing for Outside-Knowledge VQA	May 10, 2023	DecoderQuestion Answering	CodeCode Available	1	5
GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene Graph	Sep 6, 2021	Graph GenerationGraph Learning	CodeCode Available	1	5
Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment	Feb 21, 2024	Language ModellingQuestion Answering	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
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis	Jun 28, 2024	Visual Question Answering (VQA)Visual Reasoning	CodeCode Available	1	5
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?	Jan 5, 2025	Image CaptioningImage to text	CodeCode Available	1	5
An Empirical Study of Multimodal Model Merging	Apr 28, 2023	modelRetrieval	CodeCode Available	1	5
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA	Sep 10, 2021	Image CaptioningQuestion Answering	CodeCode Available	1	5
CoCa: Contrastive Captioners are Image-Text Foundation Models	May 4, 2022	Action ClassificationDecoder	CodeCode Available	1	5
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling	Sep 4, 2022	Fill MaskOptical Flow Estimation	CodeCode Available	1	5
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering	Nov 13, 2023	Decision MakingExplanation Generation	CodeCode Available	1	5
COBRA: Contrastive Bi-Modal Representation Algorithm	May 7, 2020	Cross-Modal RetrievalImage Captioning	CodeCode Available	1	5
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery	Jul 11, 2023	Question AnsweringScene Understanding	CodeCode Available	1	5
3D-Aware Visual Question Answering about Parts, Poses and Occlusions	Oct 27, 2023	Question AnsweringVisual Question Answering	CodeCode Available	1	5
FlowLearn: Evaluating Large Vision-Language Models on Flowchart Understanding	Jul 6, 2024	Optical Character Recognition (OCR)Visual Question Answering (VQA)	CodeCode Available	1	5
An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models	Nov 9, 2024	object-detectionObject Detection	CodeCode Available	1	5
Visual Grounding Methods for VQA are Working for the Wrong Reasons!	Apr 12, 2020	Question AnsweringVisual Grounding	CodeCode Available	1	5
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports	Sep 3, 2020	Image-text RetrievalMedical Visual Question Answering	CodeCode Available	1	5
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture	Jun 16, 2024	DiversityMultiple-choice	CodeCode Available	1	5
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules	May 11, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Generative Bias for Robust Visual Question Answering	Aug 1, 2022	Knowledge DistillationQuestion 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
FiLM: Visual Reasoning with a General Conditioning Layer	Sep 22, 2017	Image Retrieval with Multi-Modal QueryVisual Question Answering (VQA)	CodeCode Available	1	5
Coarse-to-Fine Reasoning for Visual Question Answering	Oct 6, 2021	Question AnsweringVisual Question Answering	CodeCode Available	1	5
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features	Jan 14, 2020	ClassificationDiversity	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