Semantic segmentation, also called scene labeling, refers to the process of assigning a
semantic label (eg car, people, and road) to each pixel of an image. It is an essential data …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

Deep convolutional neural networks (DCNNs) trained on a large number of images with
strong pixel-level annotations have recently significantly pushed the state-of-art in semantic …

We introduce a new loss function for the weakly-supervised training of semantic image
segmentation models based on three guiding principles: to seed with weak localization …

摘要目标视觉检测是计算机视觉领域的一个重要问题, 在视频监控, 自主驾驶,
人机交互等方面具有重要的研究意义和应用价值. 近年来, 深度学习在图像分类研究中取得了 …

Weakly-supervised learning based on, eg, partially labelled images or image-tags, is
currently attracting significant attention in CNN segmentation as it can mitigate the need for …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

Object category localization is a challenging problem in computer vision. Standard
supervised training requires bounding box annotations of object instances. This time …

We propose semantic texton forests, efficient and powerful new low-level features. These
are ensembles of decision trees that act directly on image pixels, and therefore do not need …

Color names are required in real-world applications such as image retrieval and image
annotation. Traditionally, they are learned from a collection of labeled color chips. These …