To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Convolutional neural networks (CNNs) are one of the main types of neural networks used for
image recognition and classification. CNNs have several uses, some of which are object …

In this paper, we develop 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 …

For years, the YOLO series has been the de facto industry-level standard for efficient object
detection. The YOLO community has prospered overwhelmingly to enrich its use in a …

Recent research on remote sensing object detection has largely focused on improving the
representation of oriented bounding boxes but has overlooked the unique prior knowledge …

Real-time object detection is one of the most important research topics in computer vision.
As new approaches regarding architecture optimization and training optimization are …

In this research, we propose a new 3D object detector with a trustworthy depth estimation,
dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work …

Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …

Abstract 3D visual perception tasks, including 3D detection and map segmentation based on
multi-camera images, are essential for autonomous driving systems. In this work, we present …