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Visual Question Answering On Vqa V2 Test Dev 1

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title	Repository
BLIP-2 ViT-G OPT 6.7B (fine-tuned)	82.30	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
CoCa	82.3	CoCa: Contrastive Captioners are Image-Text Foundation Models
OFA	82.0	OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
BLIP-2 ViT-G OPT 2.7B (fine-tuned)	81.74	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
BLIP-2 ViT-G FlanT5 XL (fine-tuned)	81.66	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
mPLUG-2	81.11	mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
Florence	80.16	Florence: A New Foundation Model for Computer Vision
Aurora (ours, r=64)	77.69	-	-
VK-OOD	76.8	Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
LXMERT (low-magnitude pruning)	70.72	LXMERT Model Compression for Visual Question Answering
LocVLM-L	56.2	Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs

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Visual Question Answering On Vqa V2 Test Dev 1 | SOTA | HyperAI