HyperAIHyperAI
HomeNewsLatest PapersTutorialsDatasetsWikiSOTALLM ModelsGPU LeaderboardEvents
Search
About
HyperAIHyperAI

Visual Reasoning On Nlvr2 Dev

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
XFM (base)87.6Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks-
VLMo85.64VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts-
VisualBERT66.7VisualBERT: A Simple and Performant Baseline for Vision and Language-
VK-OOD83.9Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis-
CoCa86.1CoCa: Contrastive Captioners are Image-Text Foundation Models-
X-VLM (base)84.41Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts-
X2-VLM (large)88.7X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks-
SOHO76.37Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning-
ALBEF (14M)83.14Align before Fuse: Vision and Language Representation Learning with Momentum Distillation-
VK-OOD84.6Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
ViLT-B/3275.7ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision-
SimVLM84.53SimVLM: Simple Visual Language Model Pretraining with Weak Supervision-
BEiT-391.51Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks-
X2-VLM (base)86.2X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks-
LXMERT (Pre-train + scratch)74.9LXMERT: Learning Cross-Modality Encoder Representations from Transformers-
0 of 15 row(s) selected.
Visual Reasoning On Nlvr2 Dev | SOTA | HyperAI