Introducing Unidentified Video Objects, a new benchmark for open-world object segmentation

We are sharing Unidentified Video Objects (UVO), a new benchmark to facilitate research on open-world segmentation, an important computer vision task that aims to detect, segment, and track all objects exhaustively in a video. While machines typically must learn specific object concepts in order to recognize them, UVO can help them mimic humans' ability to detect unfamiliar visual objects. Over the past few years, object segmentation has become one of the most active areas of research in computer vision. That's because it's key to correctly identify the objects in a scene or understand where they're located. As a result, researchers have proposed a number of different approaches for segmenting objects in visual scenes, such as Mask R-CNN and MaskProp.