**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.

**TaxoExpan** is a self-supervised taxonomy expansion framework. It automatically generates a set of <query concept, anchor concept> pairs from the existing taxonomy as training data. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. TaxoExpan features: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data.

TaxoExpan

TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

YOLOX

YOLOX: Exceeding YOLO Series in 2021

**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.

Source	YOLOX: Exceeding YOLO Series in 2021
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com