VQA Models
Modulated Residual Network
Introduced by Vries et al. in Modulating early visual processing by languageMODERN, or Modulated Residual Network, is an architecture for visual question answering (VQA). It employs conditional batch normalization to allow a linguistic embedding from an LSTM to modulate the batch normalization parameters of a ResNet. This enables the linguistic embedding to manipulate entire feature maps by scaling them up or down, negating them, or shutting them off, etc.
Source: Modulating early visual processing by language
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Question Answering | 1 | 33.33% |
| Visual Question Answering | 1 | 33.33% |
| Visual Question Answering (VQA) | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
Conditional Batch Normalization
|
Normalization | |
LSTM
|
Recurrent Neural Networks | |
ResNet
|
Convolutional Neural Networks | |
Tanh Activation
|
Activation Functions |
