Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box $M$ with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold)
with $M$ are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss. 

**Soft-NMS** solves this problem by decaying the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process.

**Temporal Activation Regularization (TAR)** is a type of slowness regularization for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) that penalizes differences between states that have been explored in the past. Formally we minimize:

$$\beta{L\_{2}}\left(h\_{t} - h\_{t+1}\right)$$

where $L\_{2}$ is the $L\_{2}$ norm, $h_{t}$ is the output of the RNN at timestep $t$, and $\beta$ is a scaling coefficient.

Temporal Activation Regularization

Revisiting Activation Regularization for Language RNNs

Soft-NMS

Soft-NMS -- Improving Object Detection With One Line of Code

A **CoordConv** layer is a simple extension to the standard convolutional layer. It has the same functional signature as a convolutional layer, but accomplishes the mapping by first concatenating extra channels to the incoming representation. These channels contain hard-coded coordinates, the most basic version of which is one channel for the $i$ coordinate and one for the $j$ coordinate.

The CoordConv layer keeps the properties of few parameters and efficient computation from convolutions, but allows the network to learn to keep or to discard translation invariance as is needed for the task being learned. This is useful for coordinate transform based tasks where regular convolutions can fail.

Source	Soft-NMS -- Improving Object Detection With One Line of Code
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com