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Visual Question Answering On Vqa V2 Val

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
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
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Visual Question Answering On Vqa V2 Val | SOTA | HyperAI