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

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 …

Open World Object Detection (OWOD) is a challenging and realistic task that extends
beyond the scope of standard Object Detection task. It involves detecting both known and …

Existing 3D instance segmentation methods typically assume that all semantic classes to be
segmented would be available during training and only seen categories are segmented at …

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 …

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

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 …

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 …