We present a novel approach to the detection of unknown objects in the context of
autonomous driving. The problem is formulated as anomaly detection, since we assume that …

The high performance of RGB-D based road segmentation methods contrasts with their rare
application in commercial autonomous driving, which is owing to two reasons: 1) the prior …

The recently proposed open-world object and open-set detection have achieved a
breakthrough in finding never-seen-before objects and distinguishing them from known …

Object detection models perform well at localizing and classifying objects that they are
shown during training. However, due to the difficulty and cost associated with creating and …

Visual obstacle discovery is a key step towards autonomous navigation of indoor mobile
robots. Successful solutions have many applications in multiple scenes. One of the …

Object detection is a fundamental problem in computer vision. Impressive results have been
achieved on largescale detection benchmarks by fully-supervised object detection (FSOD) …

Monocular depth prediction is an important task in scene understanding. It aims to predict
the dense depth of a single RGB image. With the development of deep learning, the …

Monocular road segmentation is a fundamental component of autonomous driving. High
accuracy has always been a focus of the community for this task. However, the exploration of …

Text tracking is to track multiple texts in a video, and construct a trajectory for each text.
Existing methods tackle this task by utilizing the tracking-by-detection framework, ie …

Occlusion relationship reasoning demands closed contour to express the object, and
orientation of each contour pixel to describe the order relationship between objects. Current …