Diffusion models are emerging expressive generative models, in which a large number of
time steps (inference steps) are required for a single image generation. To accelerate such …

The success of deep learning in recent years has lead to a rising demand for neural network
architecture engineering. As a consequence, neural architecture search (NAS), which aims …

In many binary segmentation tasks, most CNNs-based methods use a U-shape encoder-
decoder network as their basic structure. They ignore two key problems when the encoder …

Abstract Salient Object Detection (SOD) is a challenging problem that aims to precisely
recognize and segment the salient objects. In ground-truth maps, all pixels belonging to the …

Deep convolutional neural networks (CNNs) have been widely used in surface defect
detection. However, no CNN architecture is suitable for all detection tasks and designing …

Although weakly-supervised techniques can reduce the labeling effort, it is unclear whether
a saliency model trained with weakly-supervised data (eg, point annotation) can achieve the …

Salient object detection (SOD) has rapidly developed in recent years, and detection
performance has greatly improved. However, the price of these improvements is …

We address the challenge of training a large supernet for the object detection task, using a
relatively small amount of training data. Specifically, we propose an efficient supernet-based …

Recently, it has been shown that the traditional Image Signal Processing (ISP) can be
replaced by deep neural networks due to their superior performance. However, most of …

The estimation of 3D human body poses from 2D images is needed in a wide-ranging
application across robotics, computer graphics, and patient monitoring. While Deep …