Adaptive Early-Learning Correction for Segmentation from Noisy Annotations

**R-CNN**, or **Regions with CNN Features**, is an object detection model that uses high-capacity CNNs to bottom-up region proposals in order to localize and segment objects. It uses [selective search](https://paperswithcode.com/method/selective-search) to identify a number of bounding-box object region candidates (“regions of interest”), and then extracts features from each region independently for classification.

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation

ADELE

**Chained-Tracker**, or **CTracker**,  is an online model for multiple-object tracking. It chains paired bounding boxes regression results estimated from overlapping nodes, of which each node covers two adjacent frames. The paired regression is made attentive by object-attention (brought by a detection module) and identity-attention (ensured by an ID verification module).

The joint attention module guides the paired boxes regression branch to focus on informative spatial regions with two other branches. One is the object classification branch, which predicts the confidence scores for the first box in the detected box pairs, and such scores are used to guide the regression branch to focus on the foreground regions. The other one is the ID verification branch whose prediction facilitates the regression branch to focus on regions corresponding to the same target. Finally, the bounding box pairs are filtered according to the classification confidence. Then, the generated box pairs belonging to the adjacent frame pairs could be associated using simple methods like IoU (Intersection over Union) matching according to their boxes in the common frame. In this way, the tracking process could be achieved by chaining all the adjacent frame pairs (i.e. chain nodes) sequentially.

Source	Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
Year	2000
Data Source	Papers with Code archive (CC BY-SA)