检索结果-内蒙古大学图书馆

International Joint Conference on Neural Networks (IJCNN)

作者： Chen, PanPan Wang, DaHan Wu, JiaHua Wu, Yun He, YiFan Zhang, XuYao Zhu, ShunZhi Xiamen Univ Technol Fujian Key Lab Pattern Recognit & Image Understan Xiamen Peoples R China Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Inst Automat Beijing Peoples R China Reconova Technol Co Ltd Xiamen Key Lab Visual Percept Technol & Applicat Xiamen Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Click-based interactive image segmentation intends to segment an object from the background under user click guidance. Recently, Vision Transformer has made significant strides in interactive image segmentation. However, the previous studies 1) overlook the importance of different clicks in terms of their contribution to the segmentation results;and 2) suffer from inconsistency across different feature scales in the multi-scale structure. In this paper, we propose a new interactive segmentation framework, named RCFormer, with two novel components: reconstruct click patch embedding (RCPE) for encoding the importance of clicks, and multi-scale adaptive fusion (MSAF) for the adaptive fusion of feature maps across different scales. RCPE enhances the effectiveness of click interactions by spatially distinguishing the importance of clicks. MSAF adaptively fuses useful spatial information and filters the redundant feature at multi-scales. The experiments on several benchmarks show that our proposed approach achieves state-of-the-art performance. Notably, our method achieves 2.31 NoC@90 on the Berkeley dataset, improving by 8.6% over the previous best results.

关键词： interactive image segmentation Vision transformer Multi scale fusion

来源：评论

学校读者我要写书评

暂无评论

A Clicking Strategy Inspired by Inter-Individual Variation for interactive image segmentation 6

A Clicking Strategy Inspired by Inter-Individual Variation f...

引用

6th Artificial Intelligence and Cloud Computing Conference (AICCC) / 5th Asia Digital image Processing Conference

作者： Shuofeng, Zhao Chunzhi, Gu Jun, Yu Chao, Zhang Univ Fukui Dept Engn Fukui Japan Toyohashi Univ Technol Dept Comp Sci & Engn Toyohashi Aichi Japan Niigata Univ Inst Sci & Technol Niigata Japan

ISBN: (纸本)9798400716225

Click-based interactive segmentation is a fundamental task in computer vision that allows user clicks to refine the results. Existing works typically focus on developing powerful segmentation models, yet sparsely treating the clicking method itself. In this study, we propose a novel clicking strategy that specifically aims to reflect the inter-individual variations of different humans during training to improve segmentation results. Our method consists of three steps. In particular, we first apply the erosion operation on the ground-truth segmentation mask with different parameter settings to generate multiple eroded masks. These eroded masks are then regarded as possible hypotheses of users' interested regions. Then, we randomly select one mask from the hypotheses to simulate an arbitrary users' behavior. By next solving the visual center of the selected mask, the training clicks are eventually obtained via randomly sampling from the visual center region. In essence, our key idea is to cast multiple eroded regions as the potentially diverse users' interests, and include the resulting stochasticity into the model training for better generality. We directly adopt an existing segmentation backbone and incorporate our clicking strategy in the training to show the effectiveness of our method. Experimental results on five datasets generally demonstrate that our method contributes to state-of-the-art segmentation performance.

关键词： interactive image segmentation Click strategies Human Visual Psychology Inter-Individual variation segmentation

来源：评论

学校读者我要写书评

暂无评论

GazeSAM: interactive image segmentation with Eye Gaze and Segment Anything Model 2

GazeSAM: Interactive Image Segmentation with Eye Gaze and Se...

引用

2nd Gaze Meets Machine Learning Workshop

作者： Wang, Bin Aboah, Armstrong Zhang, Zheyuan Pan, Hongyi Bagci, Ulas Northwestern Univ Chicago IL 60611 USA

interactive image segmentation aims to assist users in efficiently generating high-quality data annotations through user-friendly interactions such as clicking, scribbling, and bounding boxes. However, mouse-based interaction methods can induce user fatigue during large-scale dataset annotation and are not entirely suitable for some domains, such as radiology. This study introduces eye gaze as a novel interactive prompt for image segmentation, different than previous model-based applications. Specifically, leveraging the real-time interactive prompting feature of the recently proposed Segment Anything Model (SAM), we present the GazeSAM system to enable users to collect target segmentation masks by simply looking at the region of interest. GazeSAM tracks users' eye gaze and utilizes it as the input prompt for SAM, generating target segmentation masks in real time. To our best knowledge, GazeSAM is the first work to combine eye gaze and SAM for interactive image segmentation. Experimental results demonstrate that GazeSAM can improve nearly 50% efficiency in 2D natural image and 3D medical image segmentation tasks. The code is available in https://***/ukaukaaaa/GazeSAM.

关键词： Eye Gaze Segment Anything Model interactive image segmentation Eye Tracking

来源：评论

学校读者我要写书评

暂无评论

SPT: Sequence Prompt Transformer for interactive image segmentation

SPT: Sequence Prompt Transformer for Interactive Image Segme...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cheng, Senlin Sun, Haopeng Xie, Tao Zhao, Hangyue Chen, Yiqiang Xu, Bolei Li, XiaoBo Ant Group Hangzhou China BAIDU Beijing China Beiiing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

interactive segmentation aims to extract objects of interest from an image based on user-provided clicks. In real-world applications, there is often a need to segment a series of images featuring the same target object. However, existing methods typically process one image at a time, failing to consider the sequential nature of the images. To overcome this limitation, we propose a novel method called Sequence Prompt Transformer (SPT), the first to utilize sequential image information for interactive segmentation. Our model comprises two key components: (1) Sequence Prompt Transformer (SPT) for acquiring information from sequence of images, clicks and masks to improve accurate. (2) Top-k Prompt Selection (TPS) selects precise prompts for SPT to further enhance the segmentation effect. Additionally, we create the ADE20KSeq benchmark to better evaluate model performance. We evaluate our approach on multiple benchmark datasets and show that our model surpasses state-of-the-art methods across all datasets. © 2025 IEEE.

关键词： computer vision interactive image segmentation

来源：评论

学校读者我要写书评

暂无评论

Boosting interactive image segmentation by Exploiting Semantic Clues

Boosting Interactive Image Segmentation by Exploiting Semant...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Wei, Qiaoqiao Zhang, Hui Yong, Jun-Hai Tsinghua Univ BNRist Sch Software Beijing Peoples R China

ISBN: (纸本)9781665468916

This paper presents a refinement framework for enhancing the accuracy of interactive image segmentation by exploiting all available semantic clues. interactive image segmentation iteratively improves segmentation masks using an input image and user annotations. The information available in this process ranges from low-level visual features like colors and textures to high-level semantic information, such as user annotations and segmentation results. Despite tremendous efforts to segment the overall object shapes, existing methods underutilize the available semantic clues, causing unsatisfactory boundary quality for segmentation masks. The proposed framework first extracts confidence guidance maps, then suppresses and lifts the predicted probabilities for confident pixels, and finally utilizes color similarities as bases and prediction confidence as guidance to refine the segmentation boundaries. Experimental results demonstrate that the framework has a low computational cost and significantly boosts existing methods on standard benchmarks.

关键词： interactive image segmentation boundary refinement semantic clues

来源：评论

学校读者我要写书评

暂无评论

interactive image segmentation based on the appearance model and orientation energy

引用

COMPUTER VISION AND image UNDERSTANDING 2022年第0期217卷 103371-103371页

作者： Qu, Shaojun Tan, Huang Li, Qiaoliang Peng, Zili Hunan Normal Univ 36 Lushan Rd Changsha 410081 Peoples R China Hunan Youmei Technol Dev Co Ltd Chuanggu Ind Pk568 Queyuan Rd Changsha Peoples R China

Tang et al. (2013) proposed a graph-based image segmentation model by minimizing the distance between the object and background appearance overlap models. This model is very effective for interactive image segmentation. However, it is prone to isolated nodes when the colors or other appearances characteristic of the object and background are very similar. To improve the performance of this algorithm and related algorithms, we add new spatial distance and contour orientation energy terms to the energy function. Accordingly, we modify the construction of the energy graph. We add terminal nodes S and T and add prior constraints. Finally, we use the pseudoflow algorithm proposed by Hochbaum to calculate the maximum flow of the new energy graph. A large number of experiments on the MSRA dataset, BSD dataset and GrabCut dataset show that the results of the proposed method are better than those of many recently proposed image segmentation methods. The code is available at https://***/powerhope/AMOE.

关键词： Graph cut Appearance model Orientation energy Pseudoflow interactive image segmentation Binary segmentation

来源：评论

学校读者我要写书评

暂无评论

Enhanced Spatial Awareness for Deep interactive image segmentation 5th

Enhanced Spatial Awareness for Deep Interactive Image Segmen...

引用

5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Li, Haochen Ni, Jinlong Li, Zhicheng Qian, Yuxiang Wang, Tao Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Spectral Imaging & Intelligent Se Nanjing 210094 Peoples R China

ISBN: (纸本)9783031189159;9783031189166

Existing deep interactive segmentation approaches can extract the desired object for the user based on simple click interaction. However, the first click provided by the user on the full image space domain is generally too local to capture the global target object, which causes them to rely on a large number of subsequent click corrections for satisfactory results. This paper explores how to strengthen the spatial awareness of user interaction especially after the first click input and increase the stability during the continuous iterative correction process. We first design an interactive cascaded localization strategy to determine the spatial range of the potential target, and then integrate this space-aware prior into a dual-stream network structure as a soft constraint for the segmentation. The above operation can increase the network's attention to the target of interest under very limited user interaction. A new training and inference strategy is also developed to completely adapt the benefit from the space-aware guidance. Furthermore, an object shape related loss is designed to better supervise the network based on user-provided prior guidance. Explicit subject, controllable correction and flexible interaction can help to significantly boost the interactive segmentation performance. The proposed method achieves state-of-the-art performance on several popular benchmarks.

关键词： interactive image segmentation Spatial awareness Multi task learning

来源：评论

学校读者我要写书评

暂无评论

Global Manifold Learning for interactive image segmentation

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 3239-3249页

作者： Wang, Tao Ji, Zexuan Yang, Jian Sun, Quansen Fu, Peng Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

This paper presents an interactive image segmen-tation algorithm, in which the segmentation problem is formulated as a global manifold learning process. Based on the principle that the label of each element depends on the influence of all the elements in the image, we extend the conventional local neighborhood or the long range regional relationships to the global relationships over the whole image. A probabilistic framework is established to measure the global effect of each element on all other elements. Based on two different manifold learning styles, the semi-global manifold learning (SGML) and the fully-global manifold learning (FGML) algorithms are proposed to capture the global geometry structure of the data manifold. SGML learns the intrinsic effects of each element separately, equivalent to a matrix diffusion process on an affinity graph. FGML learns the intrinsic effects of all elements together, equivalent to a label pair diffusion process on a higher-order tensor product graph. The global manifold learning helps to overcome the low contrast, weak boundary and texture problems. Extensive experiments on three public interactive segmentation datasets demonstrate the superior performance of the proposed algorithms both in accuracy and efficiency.

关键词： image segmentation Manifolds Estimation Tensile stress Sun Probabilistic logic Geometry interactive image segmentation global effects manifold learning label propagation

来源：评论

学校读者我要写书评

暂无评论

PseudoClick: interactive image segmentation with Click Imitation 17th

PseudoClick: Interactive Image Segmentation with Click Imita...

引用

17th European Conference on Computer Vision (ECCV)

作者： Liu, Qin Zheng, Meng Planche, Benjamin Karanam, Srikrishna Chen, Terrence Niethammer, Marc Wu, Ziyan Univ North Carolina Chapel Hill Chapel Hill NC USA United Imaging Intelligence Cambridge MA USA

ISBN: (纸本)9783031200670;9783031200687

The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i.e., by a minimal number of user clicks. Existing methods require users to provide all the clicks: by first inspecting the segmentation mask and then providing points on mislabeled regions, iteratively. We ask the question: can our model directly predict where to click, so as to further reduce the user interaction cost? To this end, we propose PseudoClick, a generic framework that enables existing segmentation networks to propose candidate next clicks. These automatically generated clicks, termed pseudo clicks in this work, serve as an imitation of human clicks to refine the segmentation mask. We build PseudoClick on existing segmentation backbones and show how the click prediction mechanism leads to improved performance. We evaluate PseudoClick on 10 public datasets from different domains and modalities, showing that our model not only outperforms existing approaches but also demonstrates strong generalization capability in cross-domain evaluation. We obtain new state-of-the-art results on several popular benchmarks, e.g., on the Pascal dataset, our model significantly outperforms existing state-of-the-art by reducing 12.4% number of clicks to achieve 85% IoU.

关键词： Click imitation interactive image segmentation Pseudo click

来源：评论

学校读者我要写书评

暂无评论

Adaptive fusion with multi-scale features for interactive image segmentation

引用

APPLIED INTELLIGENCE 2021年第8期51卷 5610-5621页

作者： Ding, Zongyuan Wang, Tao Sun, Quansen Wang, Hongyuan Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 Xiao Ling Wei St Nanjing 210094 Peoples R China Changzhou Univ Sch Informat Sci & Engn 1 Ge Hu Zhong Lu Changzhou 213164 Jiangsu Peoples R China

Multi-scale features are usually utilized to improve the performance of interactive image segmentation, however, they have varying leverages over the result of segmentation, for example, thinner segmentation results could be achieved by pixel-level features, but sensitive to image noise, and superpixel-level features could provide the semantic perception of the object, but easily lead to over-segmentations. Therefore, we propose an interactive image segmentation algorithm by adaptive fusion with multi-scale features (AFMSF). It intends on combining the multi-scale information adaptively for the segmentation via learning the influence coefficients of multi-scale features. First, multi-scale superpixel layers are generated by controlling the size of superpixels. Based on features of this multi-scale information, the similarity matrices and label priors with pixel-superpixel levels are then obtained. A fusion with diffusion strategy is designed to build the energy function by combining these multi-cues. Finally, the influence coefficient of each scale and the labeling are updated with each other until convergence. The algorithm we proposed is robust to diverse circumstances of objects, the experimental results on public interactive image segmentation datasets Graz, LHI, and MSRC validate the superior performance of the proposed method.

关键词： interactive image segmentation Semi-supervised learning Multi-scale features Fusion with diffusion

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：