We propose a new approach to interactive full-image semantic segmentation which enables quickly collecting training data for new datasets with previously unseen semantic classes (A demo is available at https://youtu.be/yUk8D5gEX-o). We leverage a key observation: propagation from labeled to unlabeled pixels does not necessarily require class-specific knowledge, but can be done purely based on appearance similarity within an image. We build on this observation and propose an approach capable of jointly propagating pixel labels from multiple classes without having explicit class-specific appearance models. To enable long-range propagation, our approach first globally measures appearance similarity between labeled and unlabeled pixels across the entire image. Then it locally integrates per-pixel measurements which improves the accuracy at boundaries and removes noisy label switches in homogeneous regions. We also design an efficient manual annotation interface that extends the traditional polygon drawing tools with a suite of additional convenient features (and add automatic propagation to it). Experiments with human annotators on the COCO Panoptic Challenge dataset show that the combination of our better manual interface and our novel automatic propagation mechanism leads to reducing annotation time by more than factor of 2x compared to polygon drawing. We also test our method on the ADE-20k and Fashionista datasets without making any dataset-specific adaptation nor retraining our model, demonstrating that it can generalize to new datasets and visual classes.
CVIT（计算机视觉与智能系统中心）；Kohli Center on Intelligent Systems (KCIS)（科利智能系统中心）；IIIT Hyderabad（海得拉巴国际信息技术学院）
Radiance Fields (RF) are popular to represent casually-captured scenes for new view synthesis and several applications beyond it. Mixed reality on personal spaces needs understanding and manipulating scenes represented as RFs, with semantic segmentation of objects as an important step. Prior segmentation efforts show promise but don't scale to complex objects with diverse appearance. We present the ISRF method to interactively segment objects with fine structure and appearance. Nearest neighbor feature matching using distilled semantic features identifies high-confidence seed regions. Bilatera…
We propose an algorithm for segmenting natural images based on texture and color information, which leverages the co-sparse analysis model for image segmentation within a convex multilabel optimization framework. As a key ingredient of this method, we introduce a novel textural similarity measure, which builds upon the co-sparse representation of image patches. We propose a Bayesian approach to merge textural similarity with information about color and location. Combined with recently developed convex multilabel optimization methods this leads to an efficient algorithm for both supervised and …
Farsee2 Technology（Farsee2科技）；Wuhan University（武汉大学）；Huazhong University of Science and Technology（华中科技大学）；University of South Carolina（南卡罗来纳大学）
Binary image segmentation plays an important role in computer vision and has been widely used in many applications such as image and video editing, object extraction, and photo composition. In this paper, we propose a novel interactive binary image segmentation method based on the Markov Random Field (MRF) framework and the fast bilateral solver (FBS) technique. Specifically, we employ the geodesic distance component to build the unary term. To ensure both computation efficiency and effective responsiveness for interactive segmentation, superpixels are used in computing geodesic distances inst…
University of South Carolina（南卡罗来纳大学）；Zhejiang University（浙江大学）；Utah State University（犹他州立大学）
One popular approach to interactively segment the foreground object of interest from an image is to annotate a bounding box that covers the foreground object. Then, a binary labeling is performed to achieve a refined segmentation. One major issue of the existing algorithms for such interactive image segmentation is their preference of an input bounding box that tightly encloses the foreground object. This increases the annotation burden, and prevents these algorithms from utilizing automatically detected bounding boxes. In this paper, we develop a new LooseCut algorithm that can handle cases w…
IME-USP（圣保罗大学数学与统计研究所）；University of São Paulo（圣保罗大学）；Methodist University of Piracicaba（皮拉西卡巴卫理公会大学）；UNIMEP（皮拉西卡巴卫理公会大学）；Ecole Nationale Supérieure des Télécommunications（国立高等电信学院）
This paper proposes a novel algorithm for the problem of structural image segmentation through an interactive model-based approach. Interaction is expressed in the model creation, which is done according to user traces drawn over a given input image. Both model and input are then represented by means of attributed relational graphs derived on the fly. Appearance features are taken into account as object attributes and structural properties are expressed as relational attributes. To cope with possible topological differences between both graphs, a new structure called the deformation graph is i…
Interactive image segmentation is a challenging task and receives increasing attention recently; however, two major drawbacks exist in interactive segmentation approaches. First, the segmentation performance of ROI-based methods is sensitive to the initial ROI: different ROIs may produce results with great difference. Second, most seed-based methods need intense interactions, and are not applicable in many cases. In this work, we generalize the Neutro-Connectedness (NC) to be independent of top-down priors of objects and to model image topology with indeterminacy measurement on image regions, …
Nanjing University（南京大学）；University of Wollongong（伍伦贡大学）；Nanjing Normal University（南京师范大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Due to low tissue contrast, irregular object appearance, and unpredictable location variation, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is considered as an important yet challenging task. In this paper, we present a novel method for interactive medical image segmentation with the following merits. (1) Our design is fundamentally different from previous pure patch-based and image-based segmentation methods. We observe that during delineation, the physician repeatedly check the inside-outside intensity changing to determine the boundary, which indicates tha…
We present a novel method of integrating motion and appearance cues for foreground object segmentation in unconstrained videos. Unlike conventional methods encoding motion and appearance patterns individually, our method puts particular emphasis on their mutual assistance. Specifically, we propose using an interactively constrained encoding (ICE) scheme to incorporate motion and appearance patterns into a graph that leads to a spatiotemporal energy optimization. The reason of utilizing ICE is that both motion and appearance cues for the same target share underlying correlative structure, thus …
We introduce a new spectral method for image segmentation that incorporates long range relationships for global appearance modeling. The approach combines two different graphs, one is a sparse graph that captures spatial relationships between nearby pixels and another is a dense graph that captures pairwise similarity between all pairs of pixels. We extend the spectral method for Normalized Cuts to this setting by combining the transition matrices of Markov chains associated with each graph. We also derive an efficient method for sparsifying the dense graph of appearance relationships. This le…
Tissue Image Analytics Centre, Department of Computer Science, University of Warwick（华威大学计算机科学系组织图像分析中心）
From the simple measurement of tissue attributes in pathology workflow to designing an explainable diagnostic/prognostic AI tool, access to accurate semantic segmentation of tissue regions in histology images is a prerequisite. However, delineating different tissue regions manually is a laborious, time-consuming and costly task that requires expert knowledge. On the other hand, the state-of-the-art automatic deep learning models for semantic segmentation require lots of annotated training data and there are only a limited number of tissue region annotated images publicly available. To obviate …
Nanjing University of Science and Technology（南京理工大学）；Huawei Inc.（华为公司）
To understand a complex action, multiple sources of information, including appearance, positional, and semantic features, need to be integrated. However, these features are difficult to be fused since they often differ significantly in modality and dimensionality. In this paper, we present a novel framework that accomplishes this goal by interactive fusion, namely, projecting features across different spaces and guiding it using an auxiliary prediction task. Specifically, we implement the framework in three steps, namely, positional-to-appearance feature extraction, semantic feature interactio…
University of Cambridge（剑桥大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Recent advances in machine learning allow us to analyze and describe the content of high-dimensional data like text, audio, images or other signals. In order to visualize that data in 2D or 3D, usually Dimensionality Reduction (DR) techniques are employed. Most of these techniques, e.g., PCA or t-SNE, produce static projections without taking into account corrections from humans or other data exploration scenarios. In this work, we propose the interactive Similarity Projection (iSP), a novel interactive DR framework based on similarity embeddings, where we form a differentiable objective based…
The automatization of Multi-Object Tracking becomes a demanding task in real unconstrained scenarios, where the algorithms have to deal with crowds, crossing people, occlusions, disappearances and the presence of visually similar individuals. In those circumstances, the data association between the incoming detections and their corresponding identities could miss some tracks or produce identity switches. In order to reduce these tracking errors, and even their propagation in further frames, this article presents a Deep Multi-Shot neural model for measuring the Degree of Appearance Similarity (…
Universidad de Zaragoza（萨拉戈萨大学）；I3A（I3A研究所）；Universidad Rey Juan Carlos（胡安·卡洛斯国王大学）
We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived a…
Tracking by detection is a common approach to solving the Multiple Object Tracking problem. In this paper we show how learning a deep similarity metric can improve three key aspects of pedestrian tracking on a multiple object tracking benchmark. We train a convolutional neural network to learn an embedding function in a Siamese configuration on a large person re-identification dataset. The offline-trained embedding network is integrated in to the tracking formulation to improve performance while retaining real-time performance. The proposed tracker stores appearance metrics while detections ar…
QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking
ETH Zurich（苏黎世联邦理工学院）；Shanghai AI Laboratory（上海人工智能实验室）；Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；UC Berkeley（加州大学伯克利分校）
提出类稠密相似性学习并构建 QDTrack，通过稠密区域对比学习和最近邻关联实现仅外观多目标跟踪，在 BDD100K MOT 上达到新最优且开销极小。
Scientific Computing and Imaging Institute（科学计算与成像研究所）；University of Utah（犹他大学）
We present a neighborhood similarity layer (NSL) which induces appearance invariance in a network when used in conjunction with convolutional layers. We are motivated by the observation that, even though convolutional networks have low generalization error, their generalization capability does not extend to samples which are not represented by the training data. For instance, while novel appearances of learned concepts pose no problem for the human visual system, feedforward convolutional networks are generally not successful in such situations. Motivated by the Gestalt principle of grouping w…
Zhejiang University（浙江大学）；Massachusetts General Hospital（麻省总医院）；Zhejiang University of Technology（浙江工业大学）
Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition syndrome that involves the central and peripheral nervous systems. Accurate detection and segmentation of neurofibromas are essential for assessing tumor burden and longitudinal tumor size changes. Automatic convolutional neural networks (CNNs) are sensitive and vulnerable as tumors' variable anatomical location and heterogeneous appearance on MRI. In this study, we propose deep interactive networks (DINs) to address the above limitations. User interactions guide the model to recognize complicated tumors and quickly a…
Creating realistic virtual assets is a time-consuming process: it usually involves an artist designing the object, then spending a lot of effort on tweaking its appearance. Intricate details and certain effects, such as subsurface scattering, elude representation using real-time BRDFs, making it impossible to fully capture the appearance of certain objects. Inspired by the recent progress of neural rendering, we propose an approach for capturing real-world objects in everyday environments faithfully and fast. We use a novel neural representation to reconstruct volumetric effects, such as trans…
DeformMaster: An Interactive Physics-Neural World Model for Deformable Objects from Videos
Nankai University（南开大学）；Zhejiang University（浙江大学）；Southern University of Science and Technology（南方科技大学）；Rightly Robotics, A4X（Rightly Robotics，A4X）；University of Science and Technology of China（中国科学技术大学）
Tel Aviv University（特拉维夫大学）；Facebook AI Research（脸书人工智能研究院）
We introduce a method for the generation of images from an input scene graph. The method separates between a layout embedding and an appearance embedding. The dual embedding leads to generated images that better match the scene graph, have higher visual quality, and support more complex scene graphs. In addition, the embedding scheme supports multiple and diverse output images per scene graph, which can be further controlled by the user. We demonstrate two modes of per-object control: (i) importing elements from other images, and (ii) navigation in the object space, by selecting an appearance …
Multiple Object Tracking (MOT) is an important task in computer vision. MOT is still challenging due to the occlusion problem, especially in dense scenes. Following the tracking-by-detection framework, we propose the Box-Plane Matching (BPM) method to improve the MOT performacne in dense scenes. First, we design the Layer-wise Aggregation Discriminative Model (LADM) to filter the noisy detections. Then, to associate remaining detections correctly, we introduce the Global Attention Feature Model (GAFM) to extract appearance feature and use it to calculate the appearance similarity between histo…
Universidade Federal de Minas Gerais (UFMG)（米纳斯吉拉斯联邦大学）；INRIA（法国国家信息与自动化研究所）
Creating plausible virtual actors from images of real actors remains one of the key challenges in computer vision and computer graphics. Marker-less human motion estimation and shape modeling from images in the wild bring this challenge to the fore. Although the recent advances on view synthesis and image-to-image translation, currently available formulations are limited to transfer solely style and do not take into account the character's motion and shape, which are by nature intermingled to produce plausible human forms. In this paper, we propose a unifying formulation for transferring appea…
Universidad de Zaragoza（萨拉戈萨大学）；I3A（I3A（萨拉戈萨大学的一个机构））；Technicolor（泰克尼科尔公司（注：Technicolor现主要从事娱乐和技术服务，此处保留常见译名））
Selecting an optimal set of icons is a crucial step in the pipeline of visual design to structure and navigate through content. However, designing the icons sets is usually a difficult task for which expert knowledge is required. In this work, to ease the process of icon set selection to the users, we propose a similarity metric which captures the properties of style and visual identity. We train a Siamese Neural Network with an online dataset of icons organized in visually coherent collections that are used to adaptively sample training data and optimize the training process. As the dataset c…
Mobile Phone based Participatory Sensing (MPPS) system involves a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. Our developed tourist subjective data collection system with Android smartphone can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM. However, the filtering rules related to photograph were n…
Carnegie Mellon University（卡内基梅隆大学）；Michigan State University（密歇根州立大学）
Accurately identifying hands in images is a key sub-task for human activity understanding with wearable first-person point-of-view cameras. Traditional hand segmentation approaches rely on a large corpus of manually labeled data to generate robust hand detectors. However, these approaches still face challenges as the appearance of the hand varies greatly across users, tasks, environments or illumination conditions. A key observation in the case of many wearable applications and interfaces is that, it is only necessary to accurately detect the user's hands in a specific situational context. Bas…
University of Oxford（牛津大学）；Technicolor Research & Innovation（Technicolor研究与创新中心）
Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines. While pixel-wise segmentation techniques can help for this task, professional rotoscoping tools rely on parametric curves that offer the artists a much better interactive control on the definition, editing and manipulation of the segments of interest. Sticking to this prevalent rotoscoping paradigm, we propose a novel framework to capture and track the visual aspect of an arbitrary object in a scene, given a first closed outline…
Neural style transfer (NST) can create impressive artworks by transferring reference style to content image. Current image-to-image NST methods are short of fine-grained controls, which are often demanded by artistic editing. To mitigate this limitation, we propose a drawing-like interactive style transfer (IST) method, by which users can interactively create a harmonious-style image. Our IST method can serve as a brush, dip style from anywhere, and then paint to any region of the target content image. To determine the action scope, we formulate a fluid simulation algorithm, which takes styles…
This paper presents a novel approach to visual tracking: Similarity Matching Ratio (SMR). The traditional approach of tracking is minimizing some measures of the difference between the template and a patch from the frame. This approach is vulnerable to outliers and drastic appearance changes and an extensive study is focusing on making the approach more tolerant to them. However, this often results in longer, corrective algo- rithms which do not solve the original problem. This paper proposes a novel approach to the definition of the tracking problems, SMR, which turns the differences into a p…
CellPilot: A unified approach to automatic and interactive segmentation in histopathology
School of Computation and Information Technology, Technical University of Munich（慕尼黑工业大学计算与信息技术学院）；Helmholtz Zentrum München - German Research Center for Environmental Health（亥姆霍兹慕尼黑中心——德国环境健康研究中心）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
Padmasri Dr.B.V Raju Institute of Technology（帕德马什里博士B.V.拉朱技术学院）；GITAM Institute of Technology（吉塔姆技术学院）；G. Narayanamma Institute of Technology and Science（G.纳拉亚南玛技术与科学学院）
Many User interactive systems are proposed all methods are trying to implement as a user friendly and various approaches proposed but most of the systems not reached to the use specifications like user friendly systems with user interest, all proposed method implemented basic techniques some are improved methods also propose but not reaching to the user specifications. In this proposed paper we concentrated on image retrieval system with in early days many user interactive systems performed with basic concepts but such systems are not reaching to the user specifications and not attracted to th…
Yonsei University（延世大学）；Chungnam National University（忠南国立大学）；Microsoft Research（微软研究院）
We present a descriptor, called fully convolutional self-similarity (FCSS), for dense semantic correspondence. To robustly match points among different instances within the same object class, we formulate FCSS using local self-similarity (LSS) within a fully convolutional network. In contrast to existing CNN-based descriptors, FCSS is inherently insensitive to intra-class appearance variations because of its LSS-based structure, while maintaining the precise localization ability of deep neural networks. The sampling patterns of local structure and the self-similarity measure are jointly learne…
Australian National University（澳大利亚国立大学）；CSIRO（联邦科学与工业研究组织）；Curtin University（科廷大学）；North South University（北南大学）
Interactive facial image manipulation attempts to edit single and multiple face attributes using a photo-realistic face and/or semantic mask as input. In the absence of the photo-realistic image (only sketch/mask available), previous methods only retrieve the original face but ignore the potential of aiding model controllability and diversity in the translation process. This paper proposes a sketch-to-image generation framework called S2FGAN, aiming to improve users' ability to interpret and flexibility of face attribute editing from a simple sketch. The proposed framework modifies the constra…
In this contribution, a semi-automatic segmentation algorithm for (medical) image analysis is presented. More precise, the approach belongs to the category of interactive contouring algorithms, which provide real-time feedback of the segmentation result. However, even with interactive real-time contouring approaches there are always cases where the user cannot find a satisfying segmentation, e.g. due to homogeneous appearances between the object and the background, or noise inside the object. For these difficult cases the algorithm still needs additional user support. However, this additional …
Hong Kong University of Science and Technology（香港科技大学）；Sun Yat-sen University（中山大学）
Unsupervised large-scale vision-language pre-training has shown promising advances on various downstream tasks. Existing methods often model the cross-modal interaction either via the similarity of the global feature of each modality which misses sufficient information, or finer-grained interactions using cross/self-attention upon visual and textual tokens. However, cross/self-attention suffers from inferior efficiency in both training and inference. In this paper, we introduce a large-scale Fine-grained Interactive Language-Image Pre-training (FILIP) to achieve finer-level alignment through a…
University of Electronic Science and Technology of China（电子科技大学）
Aiming to link natural language descriptions to specific regions in a 3D scene represented as 3D point clouds, 3D visual grounding is a very fundamental task for human-robot interaction. The recognition errors can significantly impact the overall accuracy and then degrade the operation of AI systems. Despite their effectiveness, existing methods suffer from the difficulty of low recognition accuracy in cases of multiple adjacent objects with similar appearances.To address this issue, this work intuitively introduces the human-robot interaction as a cue to facilitate the development of 3D visua…
Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V
The Netherlands Cancer Institute（荷兰癌症研究所）；Radboud University Medical Centre（拉德堡德大学医学中心）；Macao Polytechnic University（澳门理工大学）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）
TaleForge: Interactive Multimodal System for Personalized Story Creation
1 University of Science, VNU-HCM, Ho Chi Minh City, Vietnam；2 Vietnam National University, Ho Chi Minh City, Vietnam；3 Department of Computer Science \ of Dayton Ohio, United States
