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Global video object segmentation with spatial constraint module

Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Scribble-Supervised video object segmentation

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 339-353页

作者： Peiliang Huang Junwei Han Nian Liu Jun Ren Dingwen Zhang Brain and Artificial Intelligence Laboratory School of AutomationNorthwestern Polytechnical UniversityXi’an 710072China IEEE Department of Engagement Services Mohamed Bin Zayed University of Artificial IntelligenceAbuDhabiUnited Arab Emirate Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory BeijingChina

Recently,video object segmentation has received great attention in the computer vision *** of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to *** tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual ***,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and *** address this issue,this paper introduces two novel elements to learn the video object segmentation *** first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and *** other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training *** evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-*** first generate the scribble annotations from the original per-pixel ***,we train our model and compare its test performance with the baseline models and other existing *** experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.

关键词： Convolutional neural networks(CNNs) scribble self-attention video object segmentation weakly supervised

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ASDeM: Augmenting SAM With Decoupled Memory for video object segmentation

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IEEE ACCESS 2024年 12卷 73218-73227页

作者： Liu, Xiaohu Luo, Yichuang Sun, Wei Shaanxi Univ Technol Trine Engn Inst Hanzhong 723001 Peoples R China Xian Peihua Univ Dept Intelligent Sci & Engn Xian 710125 Peoples R China Shaanxi Univ Technol Sch Mech Engn Hanzhong 723001 Peoples R China

video object segmentation models have gained impressive performance, but present low interactivity with different prompts, such as click, box or text. Some models combined with SAM in a naive manner to enhance this ability, which achieve a limited performance owing to the coarse mask and inconsistent segmentation propagation. In this paper, we propose ASDeM, which augments SAM with decoupled memory, achieving high-performance on tracking and segmentation in videos. Specifically, to explore the combination of SAM and VOS model, ASDeM fully utilizes the class-agnostic features from SAM to build the memory features for VOS model, and further the object-agnostic temporal propagation with decoupled memory is applied to address the feature staleness problem and the oblivion problem of visual information. Given the prompt for the specific object in a video, people can get satisfactory segmentation and tracking results. The experiments demonstrate the effectiveness of ASDeM on public benchmarks.

关键词： Image segmentation Visualization object segmentation Task analysis object tracking Micromechanical devices Decoding videos video object segmentation segment-anything model decoupled memory

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Adaptive Selection of Reference Frames for video object segmentation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 1057-1071页

作者： Hong, Lingyi Zhang, Wei Chen, Liangyu Zhang, Wenqiang Fan, Jianping Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Lenovo Res AI Lab Beijing 100085 Peoples R China

video object segmentation is a challenging task in computer vision because the appearances of target objects might change drastically along the time in the video. To solve this problem, space-time memory (STM) networks are exploited to make use of the information from all the intermediate frames between the first frame and the current frame in the video. However, fully using the information from all the memory frames may make STM not practical for long videos. To overcome this issue, a novel method is developed in this paper to select the reference frames adaptively. First, an adaptive selection criterion is introduced to choose the reference frames with similar appearance and precise mask estimation, which can efficiently capture the rich information of the target object and overcome the challenges of appearance changes, occlusion, and model drift. Secondly, bi-matching (bi-scale and bi-direction) is conducted to obtain more robust correlations for objects of various scales and prevents multiple similar objects in the current frame from being mismatched with the same target object in the reference frame. Thirdly, a novel edge refinement technique is designed by using an edge detection network to obtain smooth edges from the outputs of edge confidence maps, where the edge confidence is quantized into ten sub-intervals to generate smooth edges step by step. Experimental results on the challenging benchmark datasets DAVIS-2016, DAVIS-2017, YouTube-VOS, and a Long-video dataset have demonstrated the effectiveness of our proposed approach to video object segmentation.

关键词： object segmentation Optical imaging Correlation Adaptation models Predictive models Bidirectional control Task analysis video object segmentation space-time memory adaptive reference selection deep convolutional networks

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Semi-automatic video object segmentation using seeded region merging and bidirectional projection

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PATTERN RECOGNITION LETTERS 2005年第5期26卷 653-662页

作者： Liu, Z Yang, J Peng, NS Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China

In this paper, we propose a novel approach to semi-automatic video object segmentation. First, an interactive video object segmentation tool is presented for the user to easily define the desired video objects in the first frame, which is user-friendly, flexible and efficient due to the proposed fast seeded region merging approach and the combination of two different ways of user interaction, i.e., marker drawing and region selection. Then, a bidirectional projection approach is proposed to automatically track the video objects in the subsequent frames, which combines forward projection and backward projection to improve the segmentation efficiency, and incorporates pixel classification with region classification in backward projection to guarantee a more reliable tracking performance. Experimental results for various types of the MPEG-4 test sequences demonstrate an efficient and faithful segmentation performance of the proposed approach. (c) 2004 Elsevier B.V. All rights reserved.

关键词： video object segmentation video object tracking seeded region merging bidirectional projection

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Unified Spatio-Temporal Dynamic Routing for Efficient video object segmentation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第5期25卷 4512-4526页

作者： Dang, Jisheng Zheng, Huicheng Xu, Xiaohao Guo, Yulan Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China Univ Michigan Robot Dept Ann Arbor MI 48104 USA Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 518000 Peoples R China Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China

Existing methods for video object segmentation (VOS) have achieved significant success by performing semantic guidance, spatial constraint, or temporal consistency. However, VOS still remains highly challenging because it is difficult to collaboratively leverage spatial constraint, temporal consistency, and semantic guidance while reducing redundant information. In this paper, we propose an efficient unified spatio-temporal dynamic routing (STDR) framework to address VOS by achieving a better spatio-temporal balance while avoiding redundancy. Specifically, our unified spatio-temporal modeling contains three paths: 1) short-term spatial path is employed to mine the spatial constraints from the previous frame;2) long-term semantic path is used to capture semantic cues from the first reference frame with ground-truth labels;3) memory queue path is designed to efficiently exploit the temporal consistency of middle frames with a compact memory bank of constant size. To enhance the input of each path, we introduce a progressive contextual memory enhancement module to exploit the contextualized memory with growing receptive fields by progressively aggregating spatial contextual information from adjacent frames for each memory frame. Furthermore, we design a dynamic memory-routed module to globally refine the outputs of our three paths for unified modeling. Enhanced by the proposed modules, our STDR achieves state-of-the-art performance with fast speed on the DAVIS 2016, DAVIS 2017 Val/Test, YouTube-VOS 2018/2019, and real-world long-video benchmarks.

关键词： video object segmentation spatio-temporal dynamic routing progressive contextual memory enhancement spatial constraint temporal consistency

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Weakly Supervised video object segmentation via Dual-attention Cross-branch Fusion

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2022年第3期13卷 46-46页

作者： Wei, Lili Lang, Congyan Liang, Liqian Feng, Songhe Wang, Tao Chen, Shidi Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Key Lab Traff Data Anal & Min 3 Shangyuancun Beijing 100044 Peoples R China

Recently, concerning the challenge of collecting large-scale explicitly annotated videos, weakly supervised video object segmentation (WSVOS) using video tags has attracted much attention. Existing WSVOS approaches follow a general pipeline including two phases, i.e., a pseudo masks generation phase and a refinement phase. To explore the intrinsic property and correlation buried in the video frames, most of them focus on the later phase by introducing optical flow as temporal information to provide more supervision. However, these optical flow-based studies are greatly affected by illumination and distortion and lack consideration of the discriminative capacity of multi-level deep features. In this article, with the goal of capturing more effective temporal information and investigating a temporal information fusion strategy accordingly, we propose a unified WSVOS model by adopting a two-branch architecture with a multi-level cross-branch fusion strategy, named as dual-attention cross-branch fusion network (DACF-Net). Concretely, the two branches of DACF-Net, i.e., a temporal prediction subnetwork (TPN) and a spatial segmentation subnetwork (SSN), are used for extracting temporal information and generating predicted segmentation masks, respectively. To perform the cross-branch fusion between TPN and SSN, we propose a dual-attention fusion module that can be plugged into the SSN flexibly. We also pose a cross-frame coherence loss (CFCL) to achieve smooth segmentation results by exploiting the coherence of masks produced by TPN and SSN. Extensive experiments demonstrate the effectiveness of proposed approach compared with the state-of-the-arts on two challenging datasets, i.e., Davis-2016 and YouTube-objects.

关键词： video object segmentation weakly supervised temporal information attention

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Unsupervised video object segmentation Based on Mixture Models and Saliency Detection

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NEURAL PROCESSING LETTERS 2020年第1期51卷 657-674页

作者： Lin, Guofeng Fan, Wentao Huaqiao Univ Dept Comp Sci & Technol Xiamen Peoples R China

In this paper, we propose an unsupervised video object segmentation approach which is mainly based on a saliency detection method and the Gaussian mixture model with Markov random field. In our approach, the saliency detection method is developed as a preprocessing technique to calculate the probability of each pixel as the target object. In contrast to traditional saliency detection methods which are normally difficult to obtain the object's precise boundary and are therefore hard to segment consistent objects, the developed saliency detection method can calculate the saliency of each frame in the video sequence and extract the position and region of the target object with more accurate object boundary. The refined extracted object region is then taken as the prior information and incorporated into the Gaussian mixture model with Markov random field to obtain the precise pixel-wise segmentation result of each frame. The effectiveness of the proposed unsupervised video object segmentation approach is validated through experimental results using both the SegTrack and the SegTrack v2 data sets.

关键词： video object segmentation Gaussian mixture model Markov random field saliency detection

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Effective online refinement for video object segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第23期78卷 33617-33631页

作者： Li, Gongyang Liu, Zhi Zhou, Xiaofei Shanghai Univ Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Hangzhou Dianzi Univ Inst Informat & Control Hangzhou 310018 Peoples R China

In this paper, we propose a novel framework, which deeply explores themotion cue and the online fine-tuning strategy to tackle the task of semi-supervised video object segmentation. First, in order to filter out the irrelevant background regions in the initial segmentation results, which are generated by an existing semi-supervised segmentation model, a motion based background suppression method is exploited to obtain the purified segmentation results. Second, a set of key frames with high-quality segmentation results are selected based on several metrics of segmentation quality in the purified segmentation results. Finally, the selected key frames are combined with the manually annotated first frame to efficiently retrain the segmentation model online, so as to obtain more accurate segmentation results. Our experimental results on two challenging datasets demonstrate that the proposed framework achieves the state-of-the-art performance.

关键词： video object segmentation Motion cue Online fine-tuning

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Space-Time Memory Networks for video object segmentation With User Guidance

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第1期44卷 442-455页

作者： Oh, Seoung Wug Lee, Joon-Young Xu, Ning Kim, Seon Joo Yonsei Univ Seoul 03722 South Korea Adobe Res San Jose CA 95110 USA

We propose a novel and unified solution for user-guided video object segmentation tasks. In this work, we consider two scenarios of user-guided segmentation: semi-supervised and interactive segmentation. Due to the nature of the problem, available cues - video frame(s) with object masks (or scribbles) - become richer with the intermediate predictions (or additional user inputs). However, the existing methods make it impossible to fully exploit this rich source of information. We resolve the issue by leveraging memory networks and learning to read relevant information from all available sources. In the semi-supervised scenario, the previous frames with object masks form an external memory, and the current frame as the query is segmented using the information in the memory. Similarly, to work with user interactions, the frames that are given user inputs form the memory that guides segmentation. Internally, the query and the memory are densely matched in the feature space, covering all the space-time pixel locations in a feed-forward fashion. The abundant use of the guidance information allows us to better handle challenges such as appearance changes and occlusions. We validate our method on the latest benchmark sets and achieve state-of-the-art performance along with a fast runtime.

关键词： video object segmentation user-guided video object segmentation semi-supervised video object segmentation interactive video object segmentation memory networks

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