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Visual Entailment On Snli Ve Val

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
CLIP-ViL80.20How Much Can CLIP Benefit Vision-and-Language Tasks?-
EVE-ROI*70.81Visual Entailment: A Novel Task for Fine-Grained Image Understanding-
OFA91.0OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework-
SimVLM86.21SimVLM: Simple Visual Language Model Pretraining with Weak Supervision-
UNITER78.98UNITER: UNiversal Image-TExt Representation Learning-
Prompt Tuning90.04Prompt Tuning for Generative Multimodal Pretrained Models-
VILLA-LARGE80.18Large-Scale Adversarial Training for Vision-and-Language Representation Learning-
CoCa87.0CoCa: Contrastive Captioners are Image-Text Foundation Models-
SOHO85.00Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning-
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Visual Entailment On Snli Ve Val | SOTA | HyperAI