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Visual Reasoning On Nlvr2 Dev

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
XFM (base)	87.6	Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks
VLMo	85.64	VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
VisualBERT	66.7	VisualBERT: A Simple and Performant Baseline for Vision and Language
VK-OOD	83.9	Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
CoCa	86.1	CoCa: Contrastive Captioners are Image-Text Foundation Models
X-VLM (base)	84.41	Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
X2-VLM (large)	88.7	X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
SOHO	76.37	Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
ALBEF (14M)	83.14	Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
VK-OOD	84.6	Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
ViLT-B/32	75.7	ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
SimVLM	84.53	SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
BEiT-3	91.51	Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
X2-VLM (base)	86.2	X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
LXMERT (Pre-train + scratch)	74.9	LXMERT: Learning Cross-Modality Encoder Representations from Transformers

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