Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next.

F2DNet, a novel two-stage object detection architecture which eliminates redundancy of classical two-stage detectors by replacing the region proposal network with focal detection network and
bounding box head with fast suppression head.

F2DNet

F2DNet: Fast Focal Detection Network for Pedestrian Detection

Internet Explorer

Internet Explorer: Targeted Representation Learning on the Open Web

**RetinaMask** is a one-stage object detection method that improves upon [RetinaNet](https://paperswithcode.com/method/retinanet) by adding the task of instance mask prediction during training, as well as an [adaptive loss](https://paperswithcode.com/method/adaptive-loss) that improves robustness to parameter choice during training, and including more difficult examples in training.

Source	Internet Explorer: Targeted Representation Learning on the Open Web
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com