Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view clustering.Graphs are usually built to outline the correlation between multi-model objects in clustering task,and multiview ...Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view clustering.Graphs are usually built to outline the correlation between multi-model objects in clustering task,and multiview graph clustering aims to learn a consensus graph that integrates the spatial property of each view.展开更多
We present the first comprehensive video polyp segmentation(VPS)study in the deep learning era.Over the years,developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-gra...We present the first comprehensive video polyp segmentation(VPS)study in the deep learning era.Over the years,developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation annotations.To address this issue,we first introduce a high-quality frame-by-frame annotated VPS dataset,named SUN-SEG,which contains 158690colonoscopy video frames from the well-known SUN-database.We provide additional annotation covering diverse types,i.e.,attribute,object mask,boundary,scribble,and polygon.Second,we design a simple but efficient baseline,named PNS+,which consists of a global encoder,a local encoder,and normalized self-attention(NS)blocks.The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations,which are then progressively refined by two NS blocks.Extensive experiments show that PNS+achieves the best performance and real-time inference speed(170 fps),making it a promising solution for the VPS task.Third,we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based comparisons.Finally,we discuss several open issues and suggest possible research directions for the VPS community.Our project and dataset are publicly available at https://github.com/GewelsJI/VPS.展开更多
基金supported by the National Natural Science Foundation of China(U21A20472,62072223)the National Key Research and Development Plan of China(2021YFB 3600503)the Natural Science Foundation of Fujian Province(2020J01130193,2020J01131199)。
文摘Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view clustering.Graphs are usually built to outline the correlation between multi-model objects in clustering task,and multiview graph clustering aims to learn a consensus graph that integrates the spatial property of each view.
基金supported by the National Natural Science Foundation of China(No.62072223)supported by the Natural Science Foundation of Fujian Province,China(No.2020J01131199)。
文摘We present the first comprehensive video polyp segmentation(VPS)study in the deep learning era.Over the years,developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation annotations.To address this issue,we first introduce a high-quality frame-by-frame annotated VPS dataset,named SUN-SEG,which contains 158690colonoscopy video frames from the well-known SUN-database.We provide additional annotation covering diverse types,i.e.,attribute,object mask,boundary,scribble,and polygon.Second,we design a simple but efficient baseline,named PNS+,which consists of a global encoder,a local encoder,and normalized self-attention(NS)blocks.The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations,which are then progressively refined by two NS blocks.Extensive experiments show that PNS+achieves the best performance and real-time inference speed(170 fps),making it a promising solution for the VPS task.Third,we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based comparisons.Finally,we discuss several open issues and suggest possible research directions for the VPS community.Our project and dataset are publicly available at https://github.com/GewelsJI/VPS.