Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-
time object detection owing to their effective balance between computational cost and …

Abstract Recently lightweight Vision Transformers (ViTs) demonstrate superior performance
and lower latency compared with lightweight Convolutional Neural Networks (CNNs) on …

We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to
combine with the segment anything model (SAM). This integration enables the detection and …

We propose DiffusionDet, a new framework that formulates object detection as a denoising
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …

In this paper, we provide the observation that too few queries assigned as positive samples
in DETR with one-to-one set matching leads to sparse supervision on the encoder's output …

One-to-one label assignment in object detection has successfully obviated the need of non-
maximum suppression (NMS) as a postprocessing and makes the pipeline end-to-end …

Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects
from novel categories beyond the base categories on which the detector is trained. Recent …

Camera-based 3D object detection in BEV (Bird's Eye View) space has drawn great
attention over the past few years. Dense detectors typically follow a two-stage pipeline by …

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 …

High-definition (HD) map provides abundant and precise static environmental information of
the driving scene, serving as a fundamental and indispensable component for planning in …