Unsupervised localization and segmentation are long-standing computer vision challenges
that involve decomposing an image into semantically-meaningful segments without any …

Producing quality segmentation masks for images is a fundamental problem in computer
vision. Recent research has explored large-scale supervised training to enable zero-shot …

Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level
weak supervision have several limitations: sparse object coverage, inaccurate object …

Being able to learn dense semantic representations of images without supervision is an
important problem in computer vision. However, despite its significance, this problem …

Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained
with noisy labels for a long enough time. To overcome this problem, we present a simple …

Compared with laborious pixel-wise dense labeling, it is much easier to label data by
scribbles, which only costs 1 2 seconds to label one image. However, using scribble labels …

Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …

显著性目标检测旨在对图像中最显著的对象进行检测和分割, 是计算机视觉任务中重要的预处理
步骤之一, 且在信息检索, 公共安全等领域均有广泛的应用. 本文对近期基于深度学习的显著性 …

Abstract Deep Supervised Salient Object Detection (SSOD) excessively relies on large-
scale annotated pixel-level labels which consume intensive labour acquiring high quality …

Despite recent progress made by self-supervised methods in representation learning with
residual networks, they still underperform supervised learning on the ImageNet classification …