Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to ...Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to obtain.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 annotation.However,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete.To address this issue,this paper introduces two novel elements to learn the video object segmentation model.The 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 background.The other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.To 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)-2017.We first generate the scribble annotations from the original per-pixel annotations.Then,we train our model and compare its test performance with the baseline models and other existing works.Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.展开更多
Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance...Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.展开更多
Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two cues.In this work,we study a novel and efficient ...Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two cues.In this work,we study a novel and efficient full-duplex strategy network(FSNet)to address this issue,by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding stage.Specifically,we introduce a relational cross-attention module(RCAM)to achieve bidirectional message propagation across embedding sub-spaces.To improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings,we adopt a bidirectional purification module after the RCAM.Extensive experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios(e.g.,motion blur and occlusion),and compares well to leading methods both for video object segmentation and video salient object detection.The project is publicly available at https://github.com/GewelsJI/FSNet.展开更多
We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory framework.To some extent,our method solves the two difficulties encountered in traditional video o...We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory framework.To 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 frames.The algorithm uses a global context(GC)module to achieve highperformance,real-time segmentation.The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time.Moreover,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 frame.The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources.We added a refinement module to the decoder to improve boundary segmentation.Our 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.展开更多
While the development of particular video segmentation algorithms has attracted considerable research interest, relatively little effort has been devoted to provide a methodology for evaluating their performance. In t...While the development of particular video segmentation algorithms has attracted considerable research interest, relatively little effort has been devoted to provide a methodology for evaluating their performance. In this paper, we propose a methodology to objectively evaluate video segmentation algorithm with ground-truth, which is based on computing the deviation of segmentation results from the reference segmentation. Four different metrics based on classification pixels, edges, relative foreground area and relative position respectively are combined to address the spatial accuracy. Temporal coherency is evaluated by utilizing the difference of spatial accuracy between successive frames. The experimental results show the feasibility of our approach. Moreover, it is computationally more efficient than previous methods. It can be applied to provide an offline ranking among different segmentation algorithms and to optimally set the parameters for a given algorithm.展开更多
With the development of the modern information society, more and more multimedia information is available. So the technology of multimedia processing is becoming the important task for the irrelevant area of scientist...With the development of the modern information society, more and more multimedia information is available. So the technology of multimedia processing is becoming the important task for the irrelevant area of scientist. Among of the multimedia, the visual informarion is more attractive due to its direct, vivid characteristic, but at the same rime the huge amount of video data causes many challenges if the video storage, processing and transmission.展开更多
基金supported in part by the National Key R&D Program of China(2017YFB0502904)the National Science Foundation of China(61876140)。
文摘Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to obtain.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 annotation.However,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete.To address this issue,this paper introduces two novel elements to learn the video object segmentation model.The 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 background.The other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.To 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)-2017.We first generate the scribble annotations from the original per-pixel annotations.Then,we train our model and compare its test performance with the baseline models and other existing works.Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.
基金supported by the National Natural Science Foundation of China (No.61872189)。
文摘Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.
基金This work was supported by the National Natural Science Foundation of China(62176169,61703077,and 62102207).
文摘Previous video object segmentation approachesmainly focus on simplex solutions linking appearance and motion,limiting effective feature collaboration between these two cues.In this work,we study a novel and efficient full-duplex strategy network(FSNet)to address this issue,by considering a better mutual restraint scheme linking motion and appearance allowing exploitation of cross-modal features from the fusion and decoding stage.Specifically,we introduce a relational cross-attention module(RCAM)to achieve bidirectional message propagation across embedding sub-spaces.To improve the model’s robustness and update inconsistent features from the spatiotemporal embeddings,we adopt a bidirectional purification module after the RCAM.Extensive experiments on five popular benchmarks show that our FSNet is robust to various challenging scenarios(e.g.,motion blur and occlusion),and compares well to leading methods both for video object segmentation and video salient object detection.The project is publicly available at https://github.com/GewelsJI/FSNet.
基金partially supported by the National Natural Science Foundation of China(Grant Nos.61802197,62072449,and 61632003)the Science and Technology Development Fund,Macao SAR(Grant Nos.0018/2019/AKP and SKL-IOTSC(UM)-2021-2023)+1 种基金the Guangdong Science and Technology Department(Grant No.2020B1515130001)University of Macao(Grant Nos.MYRG2020-00253-FST and MYRG2022-00059-FST).
文摘We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory framework.To 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 frames.The algorithm uses a global context(GC)module to achieve highperformance,real-time segmentation.The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time.Moreover,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 frame.The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources.We added a refinement module to the decoder to improve boundary segmentation.Our 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.
文摘While the development of particular video segmentation algorithms has attracted considerable research interest, relatively little effort has been devoted to provide a methodology for evaluating their performance. In this paper, we propose a methodology to objectively evaluate video segmentation algorithm with ground-truth, which is based on computing the deviation of segmentation results from the reference segmentation. Four different metrics based on classification pixels, edges, relative foreground area and relative position respectively are combined to address the spatial accuracy. Temporal coherency is evaluated by utilizing the difference of spatial accuracy between successive frames. The experimental results show the feasibility of our approach. Moreover, it is computationally more efficient than previous methods. It can be applied to provide an offline ranking among different segmentation algorithms and to optimally set the parameters for a given algorithm.
文摘With the development of the modern information society, more and more multimedia information is available. So the technology of multimedia processing is becoming the important task for the irrelevant area of scientist. Among of the multimedia, the visual informarion is more attractive due to its direct, vivid characteristic, but at the same rime the huge amount of video data causes many challenges if the video storage, processing and transmission.
