Edge detection is one of the most important and fundamental problems in the field of
computer vision and image processing. Edge contours extracted from images are widely …

Pedestrian detection and tracking have become an important field in the computer vision
research area. This growing interest, started in the last decades, might be explained by the …

Convolutional neural networks have made significant progresses in edge detection by
progressively exploring the context and semantic features. However, local details are …

Cracks are typical line structures that are of interest in many computer-vision applications. In
practice, many cracks, eg, pavement cracks, show poor continuity and low contrast, which …

Exploiting multi-scale representations is critical to improve edge detection for objects at
different scales. To extract edges at dramatically different scales, we propose a Bi …

We provide a two-way integration for the widely-adopted ControlNet by integrating external
condition generation algorithms into a single dense prediction method and by integrating its …

Pedestrian detection in crowded scenes is a challenging problem since the pedestrians
often gather together and occlude each other. In this paper, we propose a new occlusion …

In this paper, we propose an accurate edge detector using richer convolutional features
(RCF). Since objects in natural images possess various scales and aspect ratios, learning …

Data in vision domain often exhibit highly-skewed class distribution, ie, most data belong to
a few majority classes, while the minority classes only contain a scarce amount of instances …

Cracks are a growing threat to road conditions and have drawn much attention to the
construction of intelligent transportation systems. However, as the key part of an intelligent …