Image Captioning On Nocaps Val Overall

Métriques

CIDEr

Pretrain (#images)

SPICE

Résultats

Résultats de performance de divers modèles sur ce benchmark

				Paper Title
BLIP-2 ViT-G FlanT5 XL (zero-shot)	121.6	1.1B	15.8	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
BLIP-2 ViT-G OPT 6.7B (zero-shot)	121.0	1.1B	15.3	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
BLIP-2 ViT-G OPT 2.7B (zero-shot)	119.7	1.1B	15.4	BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
LEMON_large	113.4	200M	15.0	Scaling Up Vision-Language Pre-training for Image Captioning
BLIP_ViT-L	113.2	129M	14.8	BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
SimVLM	112.2	1.8B	-	SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
BLIP_CapFilt-L	109.6	129M	14.7	BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
OmniVL	107.5	14M	14.7	OmniVL:One Foundation Model for Image-Language and Video-Language Tasks
VinVL	95.5	5.7M	13.5	VinVL: Revisiting Visual Representations in Vision-Language Models
Enc-Dec	90.2	-	12.1	Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
OSCAR	80.9	345M	11.3	Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

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Image Captioning On Nocaps Val Overall | SOTA | HyperAI