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| 1 | ECharts: A declarative framework for rapid construction of web-based visualization显示文摘While there have been a dozen of authoring systems and programming toolkits for visual design and development,users who do not have programming skills,such as data analysts or interface designers,still may feel cumbersome to efficiently implement a web-based visualization.In this paper,we present ECharts,an open-sourced,web-based,cross-platform framework that supports the rapid construction of interactive visualization.The motivation is driven by three goals:easy-touse,rich built-in interactions,and high performance.The kernel of ECharts is a suite of declarative visual design language that customizes built-in chart types.The underlying streaming architecture,together with a high-performance graphics renderer based on HTML5 canvas,enables the high expandability and performance of ECharts.We report the design,implementation,and applications of ECharts with a diverse variety of examples.We compare the utility and performance of ECharts with C3.js,HighCharts,and Chart.js.Results of the experiments demonstrate the efficiency and scalability of our framework.Since the first release in June 2013,ECharts has iterated 63 versions,and attracted over 22,000 star counts and over 1700 related projects in the GitHub.ECharts is regarded as a leading visualization development tool in the world,and ranks the third in the GitHub visualization tab. | Deqing Li Honghui Mei Yi Shen Shuang Su Wenli Zhang Junting Wang Ming Zu Wei Chen | 2018 | Visual Informatics2018，2，2： | 69 |
| 2 | PCT:Point cloud transformer显示文摘The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing.This paper presents a novel framework named Point Cloud Transformer(PCT)for point cloud learning.PCT is based on Transformer,which achieves huge success in natural language processing and displays great potential in image processing.It is inherently permutation invariant for processing a sequence of points,making it well-suited for point cloud learning.To better capture local context within the point cloud,we enhance input embedding with the support of farthest point sampling and nearest neighbor search.Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification,part segmentation,semantic segmentation,and normal estimation tasks. | Meng-Hao Guo Jun-Xiong Cai Zheng-Ning Liu Tai-Jiang Mu Ralph R.Martin Shi-Min Hu | 2021 | Computational Visual Media2021，7，2： | 55 |
| 3 | Salient object detection: A survey显示文摘Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions. | Ali Borji Ming-Ming Cheng Qibin Hou Huaizu Jiang Jia Li | 2019 | Computational Visual Media2019，5，2： | 40 |
| 4 | PVT v2:Improved baselines with Pyramid Vision Transformer显示文摘Transformers have recently lead to encouraging progress in computer vision.In this work,we present new baselines by improving the original Pyramid Vision Transformer(PVT v1)by adding three designs:(i)a linear complexity attention layer,(ii)an overlapping patch embedding,and(iii)a convolutional feed-forward network.With these modifications,PVT v2 reduces the computational complexity of PVT v1 to linearity and provides significant improvements on fundamental vision tasks such as classification,detection,and segmentation.In particular,PVT v2 achieves comparable or better performance than recent work such as the Swin transformer.We hope this work will facilitate state-ofthe-art transformer research in computer vision.Code is available at http://gffzz188fe103f8f1460as5nxquxxv9w556kn6.ffgz.tsg.suse.edu.cn/whai362/PVT. | Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo Ling Shao | 2022 | Computational Visual Media2022，8，3： | 30 |
| 5 | Attention mechanisms in computer vision:A survey显示文摘Humans can naturally and effectively find salient regions in complex scenes.Motivated by this observation,attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system.Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image.Attention mechanisms have achieved great success in many visual tasks,including image classification,object detection,semantic segmentation,video understanding,image generation,3D vision,multimodal tasks,and self-supervised learning.In this survey,we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach,such as channel attention,spatial attention,temporal attention,and branch attention;a related repository http://gffzz188fe103f8f1460as5nxquxxv9w556kn6.ffgz.tsg.suse.edu.cn/MenghaoG uo/Awesome-Vision-Attentions is dedicated to collecting related work.We also suggest future directions for attention mechanism research. | Meng-Hao Guo Tian-Xing Xu Jiang-Jiang Liu Zheng-Ning Liu Peng-Tao Jiang Tai-Jiang Mu Song-Hai Zhang Ralph R.Martin Ming-Ming Cheng Shi-Min Hu | 2022 | Computational Visual Media2022，8，3： | 27 |
| 6 | Traffic signal detection and classification in street views using an attention model显示文摘Detecting small objects is a challenging task.We focus on a special case:the detection and classification of traffic signals in street views.We present a novel framework that utilizes a visual attention model to make detection more efficient,without loss of accuracy,and which generalizes.The attention model is designed to generate a small set of candidate regions at a suitable scale so that small targets can be better located and classified.In order to evaluate our method in the context of traffic signal detection,we have built a traffic light benchmark with over 15,000 traffic light instances,based on Tencent street view panoramas.We have tested our method both on the dataset we have built and the Tsinghua–Tencent 100K(TT100K)traffic sign benchmark.Experiments show that our method has superior detection performance and is quicker than the general faster RCNN object detection framework on both datasets.It is competitive with state-of-theart specialist traffic sign detectors on TT100K,but is an order of magnitude faster.To show generality,we tested it on the LISA dataset without tuning,and obtained an average precision in excess of 90%. | Yifan Lu Jiaming Lu Songhai Zhang Peter Hall | 2018 | Computational Visual Media2018，4，3： | 16 |
| 7 | FLIC: Fast linear iterative clustering with active search显示文摘In this paper, we reconsider the clustering problem for image over-segmentation from a new perspective. We propose a novel search algorithm called'active search' which explicitly considers neighbor continuity. Based on this search method, we design a back-and-forth traversal strategy and a joint assignment and update step to speed up the algorithm. Compared to earlier methods, such as simple linear iterative clustering(SLIC) and its variants, which use fixed search regions and perform the assignment and the update steps separately, our novel scheme reduces the number of iterations required for convergence,and also provides better boundaries in the oversegmentation results. Extensive evaluation using the Berkeley segmentation benchmark verifies that our method outperforms competing methods under various evaluation metrics. In particular, our method is fastest,achieving approximately 30 fps for a 481 × 321 image on a single CPU core. To facilitate further research, our code is made publicly available. | Jiaxing Zhao Ren Bo Qibin Hou Ming-Ming Cheng Paul Rosin | 2018 | Computational Visual Media2018，4，4： | 12 |
| 8 | A survey of the state-of-the-art in patch-based synthesis显示文摘This paper surveys the state-of-the-art of research in patch-based synthesis. Patch-based methods synthesize output images by copying small regions from exemplar imagery. This line of research originated from an area called 'texture synthesis', which focused on creating regular or semi-regular textures from small exemplars. However, more recently, much research has focused on synthesis of larger and more diverse imagery, such as photos, photo collections, videos, and light fields. Additionally, recent research has focused on customizing the synthesis process for particular problem domains, such as synthesizing artistic or decorative brushes, synthesis of rich materials, and synthesis for 3D fabrication. This report investigates recent papers that follow these themes, with a particular emphasis on papers published since 2009,when the last survey in this area was published. This survey can serve as a tutorial for readers who are not yet familiar with these topics, as well as provide comparisons between these papers, and highlight some open problems in this area. | Connelly Barnes Fang-Lue Zhang | 2017 | Computational Visual Media2017，3，1： | 12 |
| 9 | BING: Binarized normed gradients for objectness estimation at 300fps显示文摘Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64 D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients(BING), can be used for efficient objectness estimation, which requires only a few atomic operations(e.g., add, bitwise shift, etc.). To improve localization quality of the proposals while maintaining efficiency, we propose a novel fast segmentation method and demonstrate its effectiveness for improving BING's localization performance, when used in multithresholding straddling expansion(MTSE) postprocessing. On the challenging PASCAL VOC2007 dataset, using 1000 proposals per image and intersectionover-union threshold of 0.5, our proposal method achieves a 95.6% object detection rate and 78.6% mean average best overlap in less than 0.005 second per image. | Ming-Ming Cheng Yun Liu Wen-Yan Lin Ziming Zhang Paul L.Rosin Philip H.S.Torr | 2019 | Computational Visual Media2019，5，1： | 11 |
| 10 | RGB-D salient object detection:A survey显示文摘Salient object detection,which simulates human visual perception in locating the most significant object(s)in a scene,has been widely applied to various computer vision tasks.Now,the advent of depth sensors means that depth maps can easily be captured;this additional spatial information can boost the performance of salient object detection.Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years,an in-depth understanding of these models and the challenges in this field remains lacking.In this paper,we provide a comprehensive survey of RGBD based salient object detection models from various perspectives,and review related benchmark datasets in detail.Further,as light fields can also provide depth maps,we review salient object detection models and popular benchmark datasets from this domain too.Moreover,to investigate the ability of existing models to detect salient objects,we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models.Finally,we discuss several challenges and open directions of RGB-D based salient object detection for future research.All collected models,benchmark datasets,datasets constructed for attribute-based evaluation,and related code are publicly available at http://gffzz188fe103f8f1460as5nxquxxv9w556kn6.ffgz.tsg.suse.edu.cn/taozh2017/RGBD-SODsurvey. | Tao Zhou Deng-Ping Fan Ming-Ming Cheng Jianbing Shen Ling Shao | 2021 | Computational Visual Media2021，7，1： | 11 |
| 11 | Metaverse:Perspectives from graphics,interactions and visualization显示文摘The metaverse is a visual world that blends the physical world and digital world.At present,the development of the metaverse is still in the early stage,and there lacks a framework for the visual construction and exploration of the metaverse.In this paper,we propose a framework that summarizes how graphics,interaction,and visualization techniques support the visual construction of the metaverse and user-centric exploration.We introduce three kinds of visual elements that compose the metaverse and the two graphical construction methods in a pipeline.We propose a taxonomy of interaction technologies based on interaction tasks,user actions,feedback and various sensory channels,and a taxonomy of visualization techniques that assist user awareness.Current potential applications and future opportunities are discussed in the context of visual construction and exploration of the metaverse.We hope this paper can provide a stepping stone for further research in the area of graphics,interaction and visualization in the metaverse. | Yuheng Zhao Jinjing Jiang Yi Chen Richen Liu Yalong Yang Xiangyang Xue Siming Chen | 2022 | Visual Informatics2022，6，1： | 10 |
| 12 | Saliency guided local and global descriptors for effective action recognition显示文摘This paper presents a novel framework for human action recognition based on salient object detection and a new combination of local and global descriptors. We first detect salient objects in video frames and only extract features for such objects.We then use a simple strategy to identify and process only those video frames that contain salient objects. Processing salient objects instead of all frames not only makes the algorithm more efficient, but more importantly also suppresses the interference of background pixels. We combine this approach with a new combination of local and global descriptors, namely3D-SIFT and histograms of oriented optical flow(HOOF), respectively. The resulting saliency guided3D-SIFT–HOOF(SGSH) feature is used along with a multi-class support vector machine(SVM) classifier for human action recognition. Experiments conducted on the standard KTH and UCF-Sports action benchmarks show that our new method outperforms the competing state-of-the-art spatiotemporal feature-based human action recognition methods. | Ashwan Abdulmunem Yu-Kun Lai Xianfang Sun | 2016 | Computational Visual Media2016，2，1： | 9 |
| 13 | Towards better analysis of machine learning models:A visual analytics perspective显示文摘Interactive model analysis,the process of understanding,diagnosing,and refining a machine learning model with the help of interactive visualization,is very important for users to efficiently solve real-world artificial intelligence and data mining problems.Dramatic advances in big data analytics have led to a wide variety of interactive model analysis tasks.In this paper,we present a comprehensive analysis and interpretation of this rapidly developing area.Specifically,we classify the relevant work into three categories:understanding,diagnosis,and refinement.Each category is exemplified by recent influential work.Possible future research opportunities are also explored and discussed. | Shixia Liu Xiting Wang Mengchen Liu Jun Zhu | 2017 | Visual Informatics2017，1，1： | 9 |
| 14 | 3D modeling and motion parallax for improved videoconferencing显示文摘We consider a face-to-face videoconferencing system that uses a Kinect camera at each end of the link for 3D modeling and an ordinary2 D display for output. The Kinect camera allows a 3D model of each participant to be transmitted;the(assumed static) background is sent separately.Furthermore, the Kinect tracks the receiver's head,allowing our system to render a view of the sender depending on the receiver's viewpoint. The resulting motion parallax gives the receivers a strong impression of 3D viewing as they move, yet the system only needs an ordinary 2D display. This is cheaper than a full3 D system, and avoids disadvantages such as the need to wear shutter glasses, VR headsets, or to sit in a particular position required by an autostereo display.Perceptual studies show that users experience a greater sensation of depth with our system compared to a typical 2D videoconferencing system. | Zhe Zhu Ralph R.Martin Robert Pepperell Alistair Burleigh | 2016 | Computational Visual Media2016，2，2： | 7 |
| 15 | Color retargeting:Interactive time-varying color image composition from time-lapse sequences显示文摘In this paper,we present an interactive static image composition approach,namely color retargeting,to flexibly represent time-varying color editing effect based on time-lapse video sequences.Instead of performing precise image matting or blending techniques,our approach treats the color composition as a pixel-level resampling problem. In order to both satisfy the user's editing requirements and avoid visual artifacts,we construct a globally optimized interpolation field. This field defines from which input video frames the output pixels should be resampled.Our proposed resampling solution ensures that(i) the global color transition in the output image is as smooth as possible,(ii) the desired colors/objects specified by the user from different video frames are well preserved,and(iii) additional local color transition directions in the image space assigned by the user are also satisfied.Various examples have been shown to demonstrate that our efficient solution enables the user to easily create time-varying color image composition results. | Shao-Ping Lu Guillaume Dauphin Gauthier Lafruit Adrian Munteanu | 2015 | Computational Visual Media2015，1，4： | 7 |
| 16 | A survey on deep geometry learning:From a representation perspective显示文摘Researchers have achieved great success in dealing with 2 D images using deep learning.In recent years,3 D computer vision and geometry deep learning have gained ever more attention.Many advanced techniques for 3 D shapes have been proposed for different applications.Unlike 2 D images,which can be uniformly represented by a regular grid of pixels,3 D shapes have various representations,such as depth images,multi-view images,voxels,point clouds,meshes,implicit surfaces,etc.The performance achieved in different applications largely depends on the representation used,and there is no unique representation that works well for all applications.Therefore,in this survey,we review recent developments in deep learning for 3 D geometry from a representation perspective,summarizing the advantages and disadvantages of different representations for different applications.We also present existing datasets in these representations and further discuss future research directions. | Yun-Peng Xiao Yu-Kun Lai Fang-Lue Zhang Chunpeng Li Lin Gao | 2020 | Computational Visual Media2020，6，2： | 7 |
| 17 | Deep residual learning for denoising Monte Carlo renderings显示文摘Learning-based techniques have recently been shown to be effective for denoising Monte Carlo rendering methods. However, there remains a quality gap to state-of-the-art handcrafted denoisers. In this paper, we propose a deep residual learning based method that outperforms both state-of-the-art handcrafted denoisers and learning-based denoisers.Unlike the indirect nature of existing learning-based methods(which e.g., estimate the parameters and kernel weights of an explicit feature based filter), we directly map the noisy input pixels to the smoothed output. Using this direct mapping formulation, we demonstrate that even a simple-and-standard ResNet and three common auxiliary features(depth, normal,and albedo) are sufficient to achieve high-quality denoising. This minimal requirement on auxiliary data simplifies both training and integration of our method into most production rendering pipelines. We have evaluated our method on unseen images created by a different renderer. Consistently superior quality denoising is obtained in all cases. | Kin-Ming Wong Tien-Tsin Wong | 2019 | Computational Visual Media2019，5，3： | 6 |
| 18 | A survey of visual analytics techniques for machine learning显示文摘Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization.To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics,we systematically review259 papers published in the last ten years together with representative works before 2010.We build a taxonomy,which includes three first-level categories:techniques before model building,techniques during modeling building,and techniques after model building.Each category is further characterized by representative analysis tasks,and each task is exemplified by a set of recent influential works.We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers. | Jun Yuan Changjian Chen Weikai Yang Mengchen Liu Jiazhi Xia Shixia Liu | 2021 | Computational Visual Media2021，7，1： | 6 |
| 19 | Panorama completion for street views显示文摘This paper considers panorama images used for street views. Their viewing angle of 360° causes pixels at the top and bottom to appear stretched and warped. Although current image completion algorithms work well, they cannot be directly used in the presence of such distortions found in panoramas of street views. We thus propose a novel approach to complete such 360° panoramas using optimizationbased projection to deal with distortions. Experimental results show that our approach is efficient and provides an improvement over standard image completion algorithms. | Zhe Zhu Ralph R.Martin Shi-Min Hu | 2015 | Computational Visual Media2015，1，1： | 6 |
| 20 | Transformers in computational visual media:A survey显示文摘Transformers,the dominant architecture for natural language processing,have also recently attracted much attention from computational visual media researchers due to their capacity for long-range representation and high performance.Transformers are sequence-to-sequence models,which use a selfattention mechanism rather than the RNN sequential structure.Thus,such models can be trained in parallel and can represent global information.This study comprehensively surveys recent visual transformer works.We categorize them according to task scenario:backbone design,high-level vision,low-level vision and generation,and multimodal learning.Their key ideas are also analyzed.Differing from previous surveys,we mainly focus on visual transformer methods in low-level vision and generation.The latest works on backbone design are also reviewed in detail.For ease of understanding,we precisely describe the main contributions of the latest works in the form of tables.As well as giving quantitative comparisons,we also present image results for low-level vision and generation tasks.Computational costs and source code links for various important works are also given in this survey to assist further development. | Yifan Xu Huapeng Wei Minxuan Lin Yingying Deng Kekai Sheng Mengdan Zhang Fan Tang Weiming Dong Feiyue Huang Changsheng Xu | 2022 | Computational Visual Media2022，8，1： | 6 |
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