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 …

The detection of unknown objects is a challenging task in computer vision because,
although there are diverse real-world detection object categories, existing object-detection …

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 …

Class-agnostic object detection (OD) can be a cornerstone or a bottleneck for many
downstream vision tasks. Despite considerable advancements in bottom-up and multi-object …

Open World Object Detection (OWOD) is a novel and challenging computer vision task that
enables object detection with the ability to detect unknown objects. Existing methods …

In this work, we study one-shot video object localization problem that aims to localize
instances of unseen objects in the target video using a single query image of the object …

In the last year, universal monocular metric depth estimation (universal MMDE) has gained
considerable attention, serving as the foundation model for various multimedia tasks, such …

In the field of deep learning based computer vision, the development of deep object
detection has led to unique paradigms (eg, two-stage or set-based) and architectures (eg …

Universal object detectors aim to detect any object in any scene without human annotation,
exhibiting superior generalization. However, the current universal object detectors show …

Autonomous driving (AD) is a typical application that requires effectively exploiting
multimedia information. For AD, it is critical to ensure safety by detecting unknown objects in …