Detecting objects remains one of computer vision and image understanding applications'
most fundamental and challenging aspects. Significant advances in object detection have …

Object detection is a crucial branch of computer vision that aims to locate and classify
objects in images. Using deep convolutional neural networks (CNNs) as the primary …

In this paper, we present an open-set object detector, called Grounding DINO, by marrying
Transformer-based detector DINO with grounded pre-training, which can detect arbitrary …

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 …

The YOLO series has become the most popular framework for real-time object detection due
to its reasonable trade-off between speed and accuracy. However we observe that the …

LiDAR and camera are two important sensors for 3D object detection in autonomous driving.
Despite the increasing popularity of sensor fusion in this field, the robustness against inferior …

We present DINO (\textbf {D} ETR with\textbf {I} mproved de\textbf {N} oising anch\textbf {O} r
boxes), a state-of-the-art end-to-end object detector.% in this paper. DINO improves over …

In this paper, we develop position embedding transformation (PETR) for multi-view 3D
object detection. PETR encodes the position information of 3D coordinates into image …

We present in this paper a novel denoising training method to speedup DETR (DEtection
TRansformer) training and offer a deepened understanding of the slow convergence issue …

Image segmentation groups pixels with different semantics, eg, category or instance
membership. Each choice of semantics defines a task. While only the semantics of each task …