HyperAIHyperAI
AccueilActualitésArticles de recherche récentsTutorielsEnsembles de donnéesWikiSOTAModèles LLMClassement GPUÉvénements
Recherche
À propos
HyperAIHyperAI

Visual Question Answering On Vqa V2 Val

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Accuracy
Paper TitleRepository
MetaLM41.1Language Models are General-Purpose Interfaces
BLIP-2 ViT-G FlanT5 XXL (zero-shot)65.2BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
BLIP-2 ViT-G OPT 6.7B (zero-shot)54.3BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
Frozen29.5Multimodal Few-Shot Learning with Frozen Language Models-
BLIP-2 ViT-G FlanT5 XL (zero-shot)63.1BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
BLIP-2 ViT-L OPT 2.7B (zero-shot)50.1BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
VLKD(ViT-B/16)38.6Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation-
BLIP-2 ViT-L FlanT5 XL (zero-shot)62.6BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
PNP-VQA63.3Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
Few VLM (zero-shot)47.7A 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)53.5BLIP-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