**Feature Intertwiner** is an object detection module that leverages the features from a more reliable set to help guide the feature learning of another less reliable set. The mutual learning process helps two sets to have closer distance within the cluster in each class. The intertwiner is applied on the object detection task, where a historical buffer is proposed to address the sample missing problem during one mini-batch and the optimal transport (OT) theory is introduced to enforce the similarity among the two sets.

**YOLOX** is a single-stage object detector that makes several modifications to [YOLOv3](https://paperswithcode.com/method/yolov3) with a  [DarkNet53](https://www.paperswithcode.com/method/darknet53) backbone. Specifically, YOLO’s head is replaced with a decoupled one. For each level of [FPN](https://paperswithcode.com/method/fpn) feature, we first adopt a 1 × 1 conv layer to reduce the feature channel to 256 and then add two parallel branches with two 3 × 3 conv layers each for classification and regression tasks respectively.

Additional changes include adding Mosaic and [MixUp](https://paperswithcode.com/method/mixup) into the augmentation strategies to boost YOLOX’s performance. The anchor mechanism is also removed so YOLOX is anchor-free. Lastly, SimOTA for label assignment  -- where label assignment is formulated as an optimal transport problem via a top-k strategy.

YOLOX

YOLOX: Exceeding YOLO Series in 2021

Feature Intertwiner

Feature Intertwiner for Object Detection

Cluster-GCN is a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms.

Description and image from: [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://arxiv.org/pdf/1905.07953.pdf)

Source	Feature Intertwiner for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com