**Hardtanh** is an activation function used for neural networks:

$$ f\left(x\right) = -1 \text{ if } x < - 1 $$
$$ f\left(x\right) = x \text{ if } -1 \leq x \leq 1 $$
$$ f\left(x\right) = 1 \text{ if } x > 1 $$

It is a cheaper and more computationally efficient version of the [tanh activation](https://paperswithcode.com/method/tanh-activation).

Image Source: [Zhuan Lan](https://zhuanlan.zhihu.com/p/30385380)

**Single-Shot Multi-Object Tracker** or **SMOT**, is a tracking framework that converts any single-shot detector (SSD) model into an online multiple object tracker, which emphasizes simultaneously detecting and tracking of the object paths. Contrary to the existing tracking by detection approaches which suffer from errors made by the object detectors, SMOT adopts the recently proposed scheme of tracking by re-detection.

The proposed SMOT consists of two stages. The first stage generates temporally consecutive tracklets by exploring the temporal and spatial correlations from previous frame. The second stage performs online linking of the tracklets to generate a face track for each person (better view in color).

SMOT

SMOT: Single-Shot Multi Object Tracking

Hardtanh Activation

An **Autoencoder** is a bottleneck architecture that turns a high-dimensional input into a latent low-dimensional code (encoder), and then performs a reconstruction of the input with this latent code (the decoder).

Image: [Michael Massi](https://en.wikipedia.org/wiki/Autoencoder#/media/File:Autoencoder_schema.png)

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com