Pattern Recognition
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7.74
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#1Ye ZhangH-Index: 6
#2Yi HouH-Index: 1
Last. Shilin ZhouH-Index: 2
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#1Roxana Bujack (LANL: Los Alamos National Laboratory)H-Index: 10
#2Xinhua Zhang (LANL: Los Alamos National Laboratory)
Last. David Rogers (LANL: Los Alamos National Laboratory)H-Index: 16
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Abstract null null Moment invariants have been successfully applied to pattern detection tasks in 2D and 3D scalar, vector, and matrix valued data. However so far no flexible basis of invariants exists, i.e., no set that is optimal in the sense that it is complete and independent for every input pattern. null In this paper, we prove that a basis of moment invariants can be generated that consists of tensor contractions of not more than two different moment tensors each under the conjecture of th...
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#4Yongjian Fu (ZJU: Zhejiang University)H-Index: 4
Abstract null null In reinforcement learning (RL), the intrinsic reward estimation is necessary for policy learning when the extrinsic reward is sparse or absent. To this end, Unified Curiosity-driven Learning with Smoothed intrinsic reward Estimation (UCLSE) is proposed to address the sparse extrinsic reward problem from the perspective of completeness of intrinsic reward estimation. We further propose state distribution-aware weighting method and policy-aware weighting method to dynamically un...
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#3Weiwei Xing (Beijing Jiaotong University)H-Index: 8
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#2Yazhou Yang (National University of Defense Technology)H-Index: 6
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#1Nan Zhang (ECNU: East China Normal University)
#2Shiliang Sun (ECNU: East China Normal University)H-Index: 40
Abstract null null Multiview clustering has become an important research topic during the past decade. However, partial views of many data instances are missing in some realistic multiview learning scenarios. To handle this problem, we develop an effective incomplete multiview nonnegative representation learning (IMNRL) framework, which is suitable for incomplete multiview clustering in various situations. The IMNRL framework performs matrix factorization on multiple incomplete graphs and decomp...
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#1Bakhtiar M. Amen (University of Liverpool)H-Index: 3
Last. Thanh-Toan Do (Monash University)H-Index: 21
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Abstract null null Every day, large-scale data are continuously generated on social media as streams, such as Twitter, which inform us about all events around the world in real-time. Notably, Twitter is one of the effective platforms to update countries leaders and scientists during the coronavirus (COVID-19) pandemic. Other people have also used this platform to post their concerns about the spread of this virus and a rapid increase of death cases globally. The aim of this work is to detect ano...
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#1Hong Yang (USYD: University of Sydney)
#2Ling Chen (UTS: University of Technology, Sydney)H-Index: 26
Last. Peng Zhang (GU: Guangzhou University)H-Index: 53
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Abstract null null Attributed graphs refer to graphs where both node links and node attributes are observable for analysis. Attributed graph embedding enables joint representation learning of node links and node attributes. Different from classical graph embedding methods such as Deepwalk and node2vec that first project node links into low-dimensional vectors which are then linearly concatenated with node attribute vectors as node representation, attributed graph embedding fully explores data de...
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#1Robin Deléarde (University of Paris)H-Index: 1
#2Camille Kurtz (University of Paris)H-Index: 15
Last. Laurent Wendling (University of Paris)H-Index: 18
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Abstract null null A major challenge in scene understanding is the handling of spatial relations between objects or object parts. Several descriptors dedicated to this task already exist, such as the force histogram which is a typical example of relative position descriptor. By computing the interaction between two objects for a given force in all the directions, it gives a good overview of the configuration, and it has useful properties that can make it invariant to the 2D viewpoint. Considerin...
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#2Mohamed Ali Souibgui (Autonomous University of Barcelona)H-Index: 3
Last. Alicia Fornés (Autonomous University of Barcelona)H-Index: 23
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Abstract null null Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a null null null n...
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