**Synergistic Image and Feature Alignment** is an unsupervised domain adaptation framework that conducts synergistic alignment of domains from both image and feature perspectives. In SIFA, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features by leveraging adversarial learning in multiple aspects and with a deeply supervised mechanism. The feature encoder is shared between both adaptive perspectives to leverage their mutual benefits via end-to-end learning.

**IoU-guided NMS** is a type of non-maximum suppression that help to eliminate the suppression failure caused by the misleading classification confidences. This is achieved through using the predicted IoU instead of the classification confidence as the ranking keyword for bounding boxes. 

IoU-guided NMS

Acquisition of Localization Confidence for Accurate Object Detection

SIFA

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

**Label Quality Model** is an intermediate supervised task aimed at predicting the clean labels from noisy labels by leveraging rater features and a paired subset for supervision. The LQM technique assumes the existence of rater features and a subset of training data with both noisy and clean labels, which we call paired-subset. In real world scenarios, some level of label noise may be unavoidable. The LQM approach still works as long as the clean(er) label is less noisy than a label from a rater that is randomly selected from the pool, e.g., clean labels can be from either expert raters or aggregation of multiple raters. LQM is trained on the paired-subset using rater features and noisy label as input, and inferred on the entire training corpus. The output of LQM is used during model training as a more accurate alternative to the noisy labels.

Source	Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com