We present T-Rex2, a highly practical model for open-set object detection. Previous open-
set object detection methods relying on text prompts effectively encapsulate the abstract …

Sketches are highly expressive, inherently capturing subjective and fine-grained visual
cues. The exploration of such innate properties of human sketches has, however, been …

Cell detection is a fundamental task in computational pathology that can be used for
extracting high-level medical information from whole-slide images. For accurate cell …

Abstract You Only Look Once (YOLO) algorithms deliver state-of-the-art performance in
object detection. This research proposes a novel one-stage YOLO-based algorithm that …

In recent years, the Dataset for Object deTection in Aerial images (DOTA) dataset has
played a pivotal role in advancing object detection in aerial images (ODAI). Despite its …

Detecting small objects is often impeded by blurriness and low resolution, which poses
substantial challenges for accurately detecting and localizing such objects. In addition …

We conduct a comprehensive study on a new task named power battery detection (PBD)
which aims to localize the dense cathode and anode plates endpoints from X-ray images to …

Accurately annotating multiple 3D objects in LiDAR scenes is laborious and challenging.
While a few previous studies have attempted to leverage semi-automatic methods for cost …

Small object detection (SOD) is significant for many real-world applications, including
criminal investigation, autonomous driving and remote sensing images. SOD has been one …

Partial label learning learns from instances with weak supervision, where each instance is
associated with a set of candidate labels, among which only one is valid. Recently …