Detecting small or tiny objects is always a difficult and challenging issue in computer vision.
In this paper, we provide a latest and comprehensive survey of deep learning-based …

Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier
of p (class| pixel feature). Though straightforward, this de facto paradigm neglects the …

Abstract 3D object detection is an important task in autonomous driving to perceive the
surroundings. Despite the excellent performance, the existing 3D detectors lack the …

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our
roads. While the perception of autonomous vehicles performs well under closed-set …

Detecting out-of-distribution examples is important for safety-critical machine learning
applications such as detecting novel biological phenomena and self-driving cars. However …

Although great progress has been made in generic object detection by advanced deep
learning techniques, detecting small objects from images is still a difficult and challenging …

The inability of state-of-the-art semantic segmentation methods to detect anomaly instances
hinders them from being deployed in safety-critical and complex applications, such as …

Contemporary deep-learning object detection methods for autonomous driving usually
presume fixed categories of common traffic participants, such as pedestrians and cars. Most …

Anomaly segmentation is a critical task for driving applications, and it is approached
traditionally as a per-pixel classification problem. However, reasoning individually about …