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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Visual Question Answering On Vqa V2 Test Dev 1

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Accuracy	Paper Title	Repository
Florence	80.16	Florence: A New Foundation Model for Computer Vision	-
LXMERT (low-magnitude pruning)	70.72	LXMERT Model Compression for Visual Question Answering	-
VK-OOD	76.8	Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis	-
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	-
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	-
LocVLM-L	56.2	Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs	-
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	-
OFA	82.0	OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework	-
Aurora (ours, r=64)	77.69	-	-
CoCa	82.3	CoCa: Contrastive Captioners are Image-Text Foundation Models	-
mPLUG-2	81.11	mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video	-

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