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 641–650 of 2167 papers

Title	Date	Tasks	Status
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation	Jan 18, 2024	Caption GenerationLanguage Modeling	—Unverified
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions	May 18, 2024	Visual Question Answering (VQA)	—Unverified
Detect, Describe, Discriminate: Moving Beyond VQA for MLLM Evaluation	Sep 23, 2024	Multiple-choiceQuestion Answering	—Unverified
BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering	Dec 13, 2023	Medical Visual Question AnsweringQuestion Answering	—Unverified
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs	Apr 1, 2024	Common Sense ReasoningObject	—Unverified
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation	Jul 31, 2019	Conditional Image GenerationFew-Shot Learning	—Unverified
How good are deep models in understanding the generated images?	Aug 23, 2022	ObjectObject Recognition	—Unverified
How Much Can CLIP Benefit Vision-and-Language Tasks?	Sep 29, 2021	Question AnsweringVisual Entailment	—Unverified
DePlot: One-shot visual language reasoning by plot-to-table translation	Dec 20, 2022	Chart Question AnsweringFactual Inconsistency Detection in Chart Captioning	—Unverified
What BERT Sees: Cross-Modal Transfer for Visual Question Generation	Feb 25, 2020	Question GenerationQuestion-Generation	—Unverified

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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