SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks
State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）；School of Creative Media, City University of Hong Kong（香港城市大学创意媒体学院）；Shanghai Jiao Tong University（上海交通大学）
We introduce SketchGNN, a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph, with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.
Let Human Sketches Help: Empowering Challenging Image Segmentation Task with Freehand Sketches
School of Information Engineering, Huzhou University（湖州大学信息工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件技术学院）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计 pillar（注：此处保留原文结构，因无通用标准译法））；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）；KOKONI, Moxin (Huzhou) Tech. Co., LTD.（摩芯（湖州）科技有限公司 KOKONI）；Institute of Psychology, Chinese Academy of Sciences（中国科学院心理研究所）
Hacettepe University（哈斯特帕大学）；Ecole des Ponts（巴黎路桥学院）；Univ Gustave Eiffel（古斯塔夫·埃菲尔大学）；CNRS（法国国家科学研究中心）；Middle East Technical University（中东技术大学）
We propose a new method for producing color images from sketches. Current solutions in sketch colorization either necessitate additional user instruction or are restricted to the "paired" translation strategy. We leverage semantic image segmentation from a general-purpose panoptic segmentation network to generate an additional adversarial loss function. The proposed loss function is compatible with any GAN model. Our method is not restricted to datasets with segmentation labels and can be applied to unpaired translation tasks as well. Using qualitative, and quantitative analysis, and based on …
Hacettepe University（哈斯特帕大学）；Middle East Technical University（中东技术大学）
We introduce a new method for generating color images from sketches or edge maps. Current methods either require some form of additional user-guidance or are limited to the "paired" translation approach. We argue that segmentation information could provide valuable guidance for sketch colorization. To this end, we propose to leverage semantic image segmentation, as provided by a general purpose panoptic segmentation network, to create an additional adversarial loss function. Our loss function can be integrated to any baseline GAN model. Our method is not limited to datasets that contain segmen…
Breaking the Box: Enhancing Remote Sensing Image Segmentation with Freehand Sketches
Huzhou University（湖州大学）；Singapore University of Technology and Design（新加坡科技设计大学）；University of Science and Technology of China（中国科学技术大学）；Zhejiang University（浙江大学）；KOKONI 3D, Moxin (Huzhou) Tech. Co., LTD.（墨芯（湖州）科技有限公司（KOKONI 3D））
Sketchy Bounding-box Supervision for 3D Instance Segmentation
PCA Lab, College of Computer Science, Nankai University（南开大学计算机科学学院PCA实验室）；School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子与信息学院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）
Parsing sketches via semantic segmentation is attractive but challenging, because (i) free-hand drawings are abstract with large variances in depicting objects due to different drawing styles and skills; (ii) distorting lines drawn on the touchpad make sketches more difficult to be recognized; (iii) the high-performance image segmentation via deep learning technologies needs enormous annotated sketch datasets during the training stage. In this paper, we propose a Sketch-target deep FCN Segmentation Network(SFSegNet) for automatic free-hand sketch segmentation, labeling each sketch in a single …
Computer Vision Center, UAB（巴塞罗那自治大学计算机视觉中心）；Helsing AI（赫尔辛人工智能）
This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images. In this cross-modal setting, we first contribute with a tough-to-beat baseline that without any specific SGOL training is able to outperform the previous works on a fixed set of classes. The baseline is useful to analyze the performance of SGOL approaches based on available simple yet powerful methods. We advance prior arts by proposing a sketch-conditioned DETR (DEtection TRansformer) architecture which avoids a hard cla…
We present the first one-shot personalized sketch segmentation method. We aim to segment all sketches belonging to the same category provisioned with a single sketch with a given part annotation while (i) preserving the parts semantics embedded in the exemplar, and (ii) being robust to input style and abstraction. We refer to this scenario as personalized. With that, we importantly enable a much-desired personalization capability for downstream fine-grained sketch analysis tasks. To train a robust segmentation module, we deform the exemplar sketch to each of the available sketches of the same …
FAIR, Meta AI（元人工智能公司基础人工智能研究院）；UC Berkeley（加州大学伯克利分校）；ICSI（国际计算机科学研究所）；University of Michigan（密歇根大学）
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models. We leverage the property of self-supervised models to 'discover' objects without supervision and amplify it to train a state-of-the-art localization model without any human labels. CutLER first uses our proposed MaskCut approach to generate coarse masks for multiple objects in an image and then learns a detector on these masks using our robust loss function. We further improve the performance by self-training the model on its predictions. Compared to prior work, CutLER is si…
Massachusetts Institute of Technology（麻省理工学院）；Autodesk（欧特克）；Autodesk Research（欧特克研究院）
Encoding images as a series of high-level constructs, such as brush strokes or discrete shapes, can often be key to both human and machine understanding. In many cases, however, data is only available in pixel form. We present a method for generating images directly in a high-level domain (e.g. brush strokes), without the need for real pairwise data. Specifically, we train a "canvas" network to imitate the mapping of high-level constructs to pixels, followed by a high-level "drawing" network which is optimized through this mapping towards solving a desired image recreation or translation task.…
A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation
Shanghai University（上海大学）；Shanghai Institute for Advanced Communication and Data Science（上海先进通信与数据科学研究院）；Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）
Queen Mary University of London（伦敦玛丽女王大学）；Sun Yat-sen University（中山大学）；Shandong University（山东大学）；Simon Fraser University（西蒙弗雷泽大学）
We contribute the first large-scale dataset of scene sketches, SketchyScene, with the goal of advancing research on sketch understanding at both the object and scene level. The dataset is created through a novel and carefully designed crowdsourcing pipeline, enabling users to efficiently generate large quantities of realistic and diverse scene sketches. SketchyScene contains more than 29,000 scene-level sketches, 7,000+ pairs of scene templates and photos, and 11,000+ object sketches. All objects in the scene sketches have ground-truth semantic and instance masks. The dataset is also highly sc…
University of Chinese Academy of Sciences（中国科学院大学）；Inception Institute of Artificial Intelligence（启元人工智能研究院）；UBTech Research（优必选研究院）；ShanghaiTech University（上海科技大学）；Southern University of Science and Technology（南方科技大学）
Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks. However, most of existing methods merely consider the global contextual information and ignore the fine foreground structures, e.g., vessel, skeleton, which may contain diagnostic indicators for medical image analysis. Inspired by human painting procedure, which is composed of stroking and color rendering steps, we propose a Sketching-rendering Unconditional Generative Adversarial Network (SkrGAN) to introduce a sketch prior constraint to gui…
Daimler AG（戴姆勒股份公司）；Mercedes-Benz R&D North America（梅赛德斯-奔驰北美研发中心）；TU Darmstadt（达姆施塔特工业大学）；Microsoft HoloLens（微软全息透镜）；ETH Zurich（苏黎世联邦理工学院）
Recent progress in advanced driver assistance systems and the race towards autonomous vehicles is mainly driven by two factors: (1) increasingly sophisticated algorithms that interpret the environment around the vehicle and react accordingly, and (2) the continuous improvements of sensor technology itself. In terms of cameras, these improvements typically include higher spatial resolution, which as a consequence requires more data to be processed. The trend to add multiple cameras to cover the entire surrounding of the vehicle is not conducive in that matter. At the same time, an increasing nu…
For polarimetric SAR (PolSAR) image classification, it is a challenge to classify the aggregated terrain types, such as the urban area, into semantic homogenous regions due to sharp bright-dark variations in intensity. The aggregated terrain type is formulated by the similar ground objects aggregated together. In this paper, a polarimetric hierarchical semantic model (PHSM) is firstly proposed to overcome this disadvantage based on the constructions of a primal-level and a middle-level semantic. The primal-level semantic is a polarimetric sketch map which consists of sketch segments as the spa…
Adobe Research India（Adobe印度研究院）；Arizona State University（亚利桑那州立大学）；Carnegie Mellon University（卡内基梅隆大学）
Recently, there has been substantial progress in image synthesis from semantic labelmaps. However, methods used for this task assume the availability of complete and unambiguous labelmaps, with instance boundaries of objects, and class labels for each pixel. This reliance on heavily annotated inputs restricts the application of image synthesis techniques to real-world applications, especially under uncertainty due to weather, occlusion, or noise. On the other hand, algorithms that can synthesize images from sparse labelmaps or sketches are highly desirable as tools that can guide content creat…
A Semantic-aware Attention and Visual Shielding Network for Cloth-changing Person Re-identification
Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology（天津理工大学计算机视觉与系统教育部重点实验室）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）
