**TinaFace** is a type of face detection method that is based on generic object detection. It consists of (a) Feature Extractor: [ResNet](https://paperswithcode.com/method/resnet)-50 and 6 level [Feature Pyramid Network](https://www.paperswithcode.com/method/fpn) to extract the multi-scale features of input image; (b) an Inception block to enhance receptive field; (c) Classification Head: 5 layers [FCN](https://paperswithcode.com/method/fcn) for classification of anchors; (d) Regression Head: 5 layers [FCN](https://paperswithcode.com/method/fcn) for regression of anchors to ground-truth objects boxes; (e) IoU Aware Head: a single convolutional layer for IoU prediction.

**RMS Pooling** is a pooling operation that calculates the square mean root for patches of a feature map, and uses it to create a downsampled (pooled) feature map.  It is usually used after a convolutional layer.

$$ z_{j} = \sqrt{\frac{1}{M}\sum^{M}_{i=1}u{ij}^{2}} $$

RMS Pooling

TinaFace

TinaFace: Strong but Simple Baseline for Face Detection

**FastSpeech2** is a text-to-speech model that aims to improve upon FastSpeech by better solving the one-to-many mapping problem in TTS, i.e., multiple speech variations corresponding to the same text. It attempts to solve this problem by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs. Specifically, in FastSpeech 2, we extract duration, pitch and energy from speech waveform and directly take them as conditional inputs in training and use predicted values in inference.

The encoder converts the phoneme embedding sequence into the phoneme hidden sequence, and then the variance adaptor adds different variance information such as duration, pitch and energy into the hidden sequence, finally the mel-spectrogram decoder converts the adapted hidden sequence into mel-spectrogram sequence in parallel. FastSpeech 2 uses a feed-forward [Transformer](https://paperswithcode.com/method/transformer) block, which is a stack of [self-attention](https://paperswithcode.com/method/multi-head-attention) and 1D-[convolution](https://paperswithcode.com/method/convolution) as in FastSpeech, as the basic structure for the encoder and mel-spectrogram decoder.

Source	TinaFace: Strong but Simple Baseline for Face Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com