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.

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.

Soft-NMS

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

CoordConv

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

**Hybrid Task Cascade**, or **HTC**, is a framework for cascading in instance segmentation. It differs from [Cascade Mask R-CNN](https://paperswithcode.com/method/cascade-mask-r-cnn) in two important aspects:  (1) instead of performing cascaded refinement on the two tasks of detection and segmentation separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background.

Source	An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com