Semantic image segmentation, which becomes one of the key applications in image
processing and computer vision domain, has been used in multiple domains such as …

Support vector machines (SVMs) are a supervised classifier successfully applied in a
plethora of real-life applications. However, they suffer from the important shortcomings of …

The usage of convolutional neural networks (CNNs) for unsupervised image segmentation
was investigated in this study. Similar to supervised image segmentation, the proposed CNN …

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 …

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 …

In this paper, we present the mQA model, which is able to answer questions about the
content of an image. The answer can be a sentence, a phrase or a single word. Our model …

Weakly supervised methods usually generate localization results based on attention maps
produced by classification networks. However, the attention maps exhibit the most …

We investigate the use of convolutional neural networks (CNNs) for unsupervised image
segmentation. As in the case of supervised image segmentation, the proposed CNN assigns …

In this paper, we address the task of learning novel visual concepts, and their interactions
with other concepts, from a few images with sentence descriptions. Using linguistic context …

Recently, deep convolutional neural networks (DCNNs) have significantly promoted the
development of semantic image segmentation. However, previous works on learning the …