**PIoU Loss** is a loss function for oriented object detection which is formulated to exploit both the angle and IoU for accurate oriented bounding box regression. The PIoU loss is derived from IoU metric with a pixel-wise form.

**UFLoss**, or **Unsupervised Feature Loss**, is a patch-based unsupervised learned feature loss for deep learning (DL) based reconstructions. The UFLoss provides instance-level discrimination by mapping similar instances to similar low-dimensional feature vectors using a pre-trained mapping network (UFLoss Network). The rationale of using features from large-patches (typically 40×40 pixels for a 300×300 pixels image) is that we want the UFLoss to capture mid-level structural and semantic features instead of using small patches (typically around 10×10 pixels), which only contain local edge information. On the other hand, the authors avoid using global features due to the fact that the training set (typically around 5000 slices) is usually not large enough to capture common and general features at a large-image scale.

UFLoss

High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss

PIoU Loss

PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments

**Grab** is a sensor processing system for cashier-free shopping. Grab needs to accurately identify and track customers, and associate each shopper with items he or she retrieves from shelves. To do this, it uses a keypoint-based pose tracker as a building block for identification and tracking, develops robust feature-based face trackers, and algorithms for associating and tracking arm movements. It also uses a probabilistic framework to fuse readings from camera, weight and RFID sensors in order to accurately assess which shopper picks up which item.

Source	PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
Year	2000
Data Source	Papers with Code archive (CC BY-SA)