HyperAI超神经

首页资讯论文教程数据集百科 SOTA LLM 模型天梯 GPU 天梯顶会

中文

HyperAI超神经

Visual Question Answering On Vqa V2 Val

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称	Accuracy	Paper Title	Repository
MetaLM	41.1	Language Models are General-Purpose Interfaces	-
BLIP-2 ViT-G FlanT5 XXL (zero-shot)	65.2	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models	-
BLIP-2 ViT-G OPT 6.7B (zero-shot)	54.3	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models	-
Frozen	29.5	Multimodal Few-Shot Learning with Frozen Language Models	-
BLIP-2 ViT-G FlanT5 XL (zero-shot)	63.1	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models	-
BLIP-2 ViT-L OPT 2.7B (zero-shot)	50.1	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models	-
VLKD(ViT-B/16)	38.6	Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation	-
BLIP-2 ViT-L FlanT5 XL (zero-shot)	62.6	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models	-
PNP-VQA	63.3	Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training	-
Few VLM (zero-shot)	47.7	A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models	-
BLIP-2 ViT-G OPT 2.7B (zero-shot)	53.5	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models	-

0 of 11 row(s) selected.

Visual Question Answering On Vqa V2 Val | SOTA | HyperAI超神经