ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, pro-cessing both visual and textual inputs in separate streams that interact through co-attentional transformer layers... (read more)

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Methods used in the Paper


METHOD TYPE
ViLBERT
Representation Learning
Residual Connection
Skip Connections
Attention Dropout
Regularization
Linear Warmup With Linear Decay
Learning Rate Schedules
Weight Decay
Regularization
BPE
Subword Segmentation
GELU
Activation Functions
Dense Connections
Feedforward Networks
Label Smoothing
Regularization
ReLU
Activation Functions
Adam
Stochastic Optimization
WordPiece
Subword Segmentation
Softmax
Output Functions
Dropout
Regularization
Multi-Head Attention
Attention Modules
Layer Normalization
Normalization
Scaled Dot-Product Attention
Attention Mechanisms
Transformer
Transformers
BERT
Language Models