The grid-centric perception is a crucial field for mobile robot perception and navigation.
Nonetheless, the grid-centric perception is less prevalent than object-centric perception as …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

We show that classifiers trained with random region proposals achieve state-of-the-art Open-
world Object Detection (OWOD): they can not only maintain the accuracy of the known …

Lifelong person re-identification (LReID) suffers from the catastrophic forgetting problem
when learning from non-stationary data. Existing exemplar-based and knowledge distillation …

Exploring new knowledge is a fundamental human ability that can be mirrored in the
development of deep neural networks, especially in the field of object detection. Open world …

Open world object detection aims to identify objects of unseen categories and incrementally
recognize them once their annotations are provided. In distinction to the traditional paradigm …

This paper addresses an important and valuable open-world object detection (OWOD) in
autonomous driving scenarios, which aims to detect objects under both domain-agnostic …

Object detection (OD) is crucial to autonomous driving. On the other hand, unknown objects,
which have not been seen in training sample set, are one of the reasons that hinder …

Plant disease detection has made significant strides thanks to the emergence of deep
learning. However, existing methods have been limited to closed-set and static learning …