**Serf**, or **Log-Softplus ERror activation Function**, is a type of activation function which is self-regularized and nonmonotonic in nature. It belongs to the [Swish](https://paperswithcode.com/method/swish) family of functions. Serf is defined as:

$$f\left(x\right) = x\text{erf}\left(\ln\left(1 + e^{x}\right)\right)$$

XGPT is a method of cross-modal generative pre-training for image captioning designed to pre-train text-to-image caption generators through three novel generation tasks, including image-conditioned masked language modeling (IMLM), image-conditioned denoising autoencoding (IDA), and text-conditioned image feature generation (TIGF). The pre-trained XGPT can be fine-tuned without any task-specific architecture modifications and build strong image captioning models.

XGPT

XGPT: Cross-modal Generative Pre-Training for Image Captioning

Serf

SERF: Towards better training of deep neural networks using log-Softplus ERror activation Function

A **Pyramid Pooling Module** is a module for semantic segmentation which acts as an effective global contextual prior. The motivation is that the problem of using a convolutional network like a [ResNet](https://paperswithcode.com/method/resnet) is that, while the receptive field is already larger than the input image, the empirical receptive field is much smaller than the theoretical one especially on high-level layers. This makes many networks not sufficiently incorporate the momentous global scenery prior. 

The PPM is an effective global prior representation that addresses this problem. It contains information with different scales and varying among different sub-regions. Using our 4-level pyramid, the pooling kernels cover the whole, half of, and small portions of the image. They are fused as the global prior. Then we concatenate the prior with the original feature map in the final part.

Source	SERF: Towards better training of deep neural networks using log-Softplus ERror activation Function
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com