Object detection or localization is an incremental step in progression from coarse to fine
digital image inference. It not only provides the classes of the image objects, but also …

Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

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 …

Object detection with transformers (DETR) reaches competitive performance with Faster R-
CNN via a transformer encoder-decoder architecture. Inspired by the great success of pre …

Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively
model linguistic rules in end-to-end deep networks remains a research challenge. In this …

We present a new method that views object detection as a direct set prediction problem. Our
approach streamlines the detection pipeline, effectively removing the need for many hand …

Prevalent state-of-the-art instance segmentation methods fall into a query-based scheme, in
which instance masks are derived by querying the image feature using a set of instance …

In this work, we design a simple, direct, and fast framework for instance segmentation with
strong performance. To this end, we propose a novel and effective approach, termed …

We present a new, embarrassingly simple approach to instance segmentation. Compared to
many other dense prediction tasks, eg, semantic segmentation, it is the arbitrary number of …

Instance segmentation is one of the fundamental vision tasks. Recently, fully convolutional
instance segmentation methods have drawn much attention as they are often simpler and …