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Visual Entailment On Snli Ve Val

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
CLIP-ViL	80.20	How Much Can CLIP Benefit Vision-and-Language Tasks?
EVE-ROI*	70.81	Visual Entailment: A Novel Task for Fine-Grained Image Understanding
OFA	91.0	OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
SimVLM	86.21	SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
UNITER	78.98	UNITER: UNiversal Image-TExt Representation Learning
Prompt Tuning	90.04	Prompt Tuning for Generative Multimodal Pretrained Models
VILLA-LARGE	80.18	Large-Scale Adversarial Training for Vision-and-Language Representation Learning
CoCa	87.0	CoCa: Contrastive Captioners are Image-Text Foundation Models
SOHO	85.00	Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

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