HyperAI
Home
News
Latest Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
English
HyperAI
Toggle sidebar
Search the site…
⌘
K
Home
SOTA
Visual Question Answering (VQA)
Visual Question Answering On Vqa V2 Test Dev
Visual Question Answering On Vqa V2 Test Dev
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
Repository
ONE-PEACE
82.6
ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
Pythia v0.3 + LoRRA
69.21
Towards VQA Models That Can Read
mPLUG (Huge)
82.43
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
X-VLM (base)
78.22
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
BEiT-3
84.19
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
Prismer
78.43
Prismer: A Vision-Language Model with Multi-Task Experts
CFR
72.5
Coarse-to-Fine Reasoning for Visual Question Answering
MUTAN
67.42
MUTAN: Multimodal Tucker Fusion for Visual Question Answering
Flamingo 80B
56.3
Flamingo: a Visual Language Model for Few-Shot Learning
Image features from bottom-up attention (adaptive K, ensemble)
69.87
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
MMU
81.26
Achieving Human Parity on Visual Question Answering
-
ALBEF (14M)
75.84
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
Oscar
73.82
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
SimVLM
80.03
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
BLIP-2 ViT-G OPT 2.7B (zero-shot)
52.3
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
VK-OOD
77.9
Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
ViLT-B/32
71.26
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
MCAN+VC
71.21
Visual Commonsense R-CNN
BLIP-2 ViT-L FlanT5 XL (zero-shot)
62.3
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
BLIP-2 ViT-L OPT 2.7B (zero-shot)
49.7
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
0 of 56 row(s) selected.
Previous
Next