Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …

As a common image editing operation, image composition aims to combine the foreground
from one image and another background image, resulting in a composite image. However …

Existing state-of-the-art salient object detection networks rely on aggregating multi-level
features of pre-trained convolutional neural networks (CNNs). However, compared to high …

Person re-identification (reID) is an important task that requires to retrieve a person's images
from an image dataset, given one image of the person of interest. For learning robust person …

Transparent and semi-transparent materials pose significant challenges for existing scene
understanding and segmentation algorithms due to their lack of RGB texture which impedes …

A key challenge of infrared small object segmentation (ISOS) is to balance miss detection
(MD) and false alarm (FA). This usually needs" opposite" strategies to suppress the two …

Understanding shadows from a single image consists of two types of task in previous
studies, containing shadow detection and shadow removal. In this paper, we present a multi …

This paper presents a network to detect shadows by exploring and combining global context
in deep layers and local context in shallow layers of a deep convolutional neural network …

Synthesizing photo-realistic images from text descriptions is a challenging problem.
Previous studies have shown remarkable progresses on visual quality of the generated …

The objective of this work is to segment high-resolution images without overloading GPU
memory usage or losing the fine details in the output segmentation map. The memory …