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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
-
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