Self-attention is of vital importance in semantic segmentation as it enables modeling of long-range context, which translates into improved performance. We argue that it is equally important to model short-range context, especially to tackle cases where not only the regions of interest are small and ambiguous, but also when there exists an imbalance between the semantic classes. To this end, we propose Masked Supervised Learning (MaskSup), an effective single-stage learning paradigm that models both short- and long-range context, capturing the contextual relationships between pixels via random masking. Experimental results demonstrate the competitive performance of MaskSup against strong baselines in both binary and multi-class segmentation tasks on three standard benchmark datasets, particularly at handling ambiguous regions and retaining better segmentation of minority classes with no added inference cost. In addition to segmenting target regions even when large portions of the input are masked, MaskSup is also generic and can be easily integrated into a variety of semantic segmentation methods. We also show that the proposed method is computationally efficient, yielding an improved performance by 10\% on the mean intersection-over-union (mIoU) while requiring $3\times$ less learnable parameters.

Cartoons are an important part of our entertainment culture. Though drawing a cartoon is not for everyone, creating it using an arrangement of basic geometric primitives that approximates that character is a fairly frequent technique in art. The key motivation behind this technique is that human bodies - as well as cartoon figures - can be split down into various basic geometric primitives. Numerous tutorials are available that demonstrate how to draw figures using an appropriate arrangement of fundamental shapes, thus assisting us in creating cartoon characters. This technique is very beneficial for children in terms of teaching them how to draw cartoons. In this paper, we develop a tool - shape2toon - that aims to automate this approach by utilizing a generative adversarial network which combines geometric primitives (i.e. circles) and generate a cartoon figure (i.e. Mickey Mouse) depending on the given approximation. For this purpose, we created a dataset of geometrically represented cartoon characters. We apply an image-to-image translation technique on our dataset and report the results in this paper. The experimental results show that our system can generate cartoon characters from input layout of geometric shapes. In addition, we demonstrate a web-based tool as a practical implication of our work.

Previous virtual try-on methods usually focus on aligning a clothing item with a person, limiting their ability to exploit the complex pose, shape and skin color of the person, as well as the overall structure of the clothing, which is vital to photo-realistic virtual try-on. To address this potential weakness, we propose a fill in fabrics (FIFA) model, a self-supervised conditional generative adversarial network based framework comprised of a Fabricator and a unified virtual try-on pipeline with a Segmenter, Warper and Fuser. The Fabricator aims to reconstruct the clothing image when provided with a masked clothing as input, and learns the overall structure of the clothing by filling in fabrics. A virtual try-on pipeline is then trained by transferring the learned representations from the Fabricator to Warper in an effort to warp and refine the target clothing. We also propose to use a multi-scale structural constraint to enforce global context at multiple scales while warping the target clothing to better fit the pose and shape of the person. Extensive experiments demonstrate that our FIFA model achieves state-of-the-art results on the standard VITON dataset for virtual try-on of clothing items, and is shown to be effective at handling complex poses and retaining the texture and embroidery of the clothing.

语言是个人表达思想的方法。每种语言都有自己的字母和数字字符集。人们可以通过口头或书面交流相互交流。但是，每种语言都有同类语言。聋哑和/或静音的个人通过手语交流。孟加拉语还具有手语，称为BDSL。数据集是关于孟加拉手册图像的。该系列包含49个单独的孟加拉字母图像。 BDSL49是一个数据集，由29,490张具有49个标签的图像组成。在数据收集期间，已经记录了14个不同成年人的图像，每个人都有不同的背景和外观。在准备过程中，已经使用了几种策略来消除数据集中的噪声。该数据集可免费提供给研究人员。他们可以使用机器学习，计算机视觉和深度学习技术开发自动化系统。此外，该数据集使用了两个模型。第一个是用于检测，而第二个是用于识别。

尽管是世界上最口语（基于人口的6 ^ {Th} $ 6 ^ {Th}），但与其他突出语言相比，还没有探讨关于孟加拉手写的图形（书写系统的最小功能单位）分类。此外，孟加拉语中的大量标记组合使得该分类任务非常具有挑战性。随着贡献这一研究问题，我们参加了一个拍手{kaggle_link}的挑战是分别分类图像中的孟加拉图格图的三个组成元素：石墨烯根，元音形象和辅音又辅音。我们探讨了一些现有神经网络模型的表演，例如多层的Perceptron（MLP）和艺术resnet50的状态。为了进一步提高性能，我们提出了我们自己的卷积神经网络（CNN）模型，用于孟加拉图形分类，具有验证根精度95.32 \％，元音精度98.61 \％，并辅音精度为98.76 \％。我们还使用VGGNet探索区域提案网络（RPN），其中设置有限的设置，可以成为提高性能的潜在未来方向。