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 601–610 of 2167 papers

Title	Date	Tasks	Status	Hype
Multi-Modal Hallucination Control by Visual Information Grounding	Mar 20, 2024	HallucinationVisual Question Answering (VQA)	—Unverified	0
WoLF: Wide-scope Large Language Model Framework for CXR Understanding	Mar 19, 2024	AnatomyInstruction Following	—Unverified	0
VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning	Mar 19, 2024	BenchmarkingImage Captioning	CodeCode Available	2
HYDRA: A Hyper Agent for Dynamic Compositional Visual Reasoning	Mar 19, 2024	Reinforcement Learning (RL)Visual Grounding	CodeCode Available	1
SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors	Mar 18, 2024	HallucinationMotion Planning	—Unverified	0
FlexCap: Describe Anything in Images in Controllable Detail	Mar 18, 2024	AttributeDense Captioning	—Unverified	0
Few-Shot VQA with Frozen LLMs: A Tale of Two Approaches	Mar 17, 2024	Image CaptioningQuestion Answering	—Unverified	0
PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset	Mar 17, 2024	AttributeCommon Sense Reasoning	CodeCode Available	1
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning	Mar 15, 2024	HallucinationInstruction Following	—Unverified	0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models	Mar 15, 2024	Few-Shot Image Classificationimage-classification	—Unverified	0

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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	InternVL-C	Accuracy	81.2	—	Unverified
10	Lyrics	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