**Siren**, or **Sinusoidal Representation Network**, is a periodic activation function for implicit neural representations. Specifically it uses the sine as a periodic activation function:

$$ \Phi\left(x\right) = \textbf{W}\_{n}\left(\phi\_{n-1} \circ \phi\_{n-2} \circ \dots \circ \phi\_{0} \right) $$

Rana Mostafa, Hoda Baraka and AbdelMoniem Bayoumi

**LMOT**, i.e., Light-weight Multi-Object Tracker,  performs joint pedestrian detection and tracking. LMOT introduces a simplified DLA-34 encoder network to extract detection features for the current image that are computationally efficient. Furthermore, we generate efficient tracking features using a linear transformer for the prior image frame and its corresponding detection heatmap. After that, LMOT fuses both detection and tracking feature maps in a multi-layer scheme and performs a two-stage online data association relying on the Kalman filter to generate tracklets. We evaluated our model on the challenging real-world MOT16/17/20 datasets, showing LMOT significantly outperforms the state-of-the-art trackers concerning runtime while maintaining high robustness. LMOT is approximately ten times faster than state-of-the-art trackers while being only 3.8% behind in performance accuracy on average leading to a much computationally lighter model.

Code: https://github.com/RanaMostafaAbdElMohsen/LMOT
Paper: https://doi.org/10.1109/ACCESS.2022.3197157

LMOT

Siren

Implicit Neural Representations with Periodic Activation Functions

**SimpleNet** is a convolutional neural network with 13 layers. The network employs a homogeneous design utilizing 3 × 3 kernels for convolutional layer and 2 × 2 kernels for pooling operations. The only layers which do not use 3 × 3 kernels are 11th and 12th layers, these layers, utilize 1 × 1 convolutional kernels. Feature-map down-sampling is carried out using nonoverlaping 2 × 2 max-pooling. In order to cope with the problem of vanishing gradient and also over-fitting, SimpleNet also uses batch-normalization with moving average fraction of 0.95 before any [ReLU](https://paperswithcode.com/method/relu) non-linearity.

Source	Implicit Neural Representations with Periodic Activation Functions
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com