Integrated generalized zero-shot learning for fine-grained classification
- Shermin, Tasfia, Teng, Shyh, Sohel, Ferdous, Murshed, Manzur, Lu, Guojun
- Authors: Shermin, Tasfia , Teng, Shyh , Sohel, Ferdous , Murshed, Manzur , Lu, Guojun
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 122, no. (2022), p.
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- Description: Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets. © 2021 Elsevier Ltd
- Authors: Shermin, Tasfia , Teng, Shyh , Sohel, Ferdous , Murshed, Manzur , Lu, Guojun
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 122, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets. © 2021 Elsevier Ltd
How to optimize an academic team when the outlier member is leaving?
- Yu, Shuo, Liu, Jiaying, Wei, Haoran, Xia, Feng, Tong, Hanghang
- Authors: Yu, Shuo , Liu, Jiaying , Wei, Haoran , Xia, Feng , Tong, Hanghang
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Intelligent Systems Vol. 36, no. 3 (May-Jun 2021), p. 23-30
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- Description: An academic team is a highly cohesive collaboration group of scholars, which has been recognized as an effective way to improve scientific output in terms of both quality and quantity. However, the high staff turnover brings about a series of problems that may have negative influences on team performance. To address this challenge, we first detect the tendency of the member who may potentially leave. Here, the outlierness is defined with respect to familiarity, which is quantified by using collaboration intensity. It is assumed that if a team member has a higher familiarity with scholars outside the team, then this member might probably leave the team. To minimize the influence caused by the leaving of such an outlier member, we propose an optimization solution to find a proper candidate who can replace the outlier member. Based on random walk with graph kernel, our solution involves familiarity matching, skill matching, as well as structure matching. The proposed approach proves to be effective and outperforms existing methods when applied to computer science academic teams.
- Authors: Yu, Shuo , Liu, Jiaying , Wei, Haoran , Xia, Feng , Tong, Hanghang
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Intelligent Systems Vol. 36, no. 3 (May-Jun 2021), p. 23-30
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- Description: An academic team is a highly cohesive collaboration group of scholars, which has been recognized as an effective way to improve scientific output in terms of both quality and quantity. However, the high staff turnover brings about a series of problems that may have negative influences on team performance. To address this challenge, we first detect the tendency of the member who may potentially leave. Here, the outlierness is defined with respect to familiarity, which is quantified by using collaboration intensity. It is assumed that if a team member has a higher familiarity with scholars outside the team, then this member might probably leave the team. To minimize the influence caused by the leaving of such an outlier member, we propose an optimization solution to find a proper candidate who can replace the outlier member. Based on random walk with graph kernel, our solution involves familiarity matching, skill matching, as well as structure matching. The proposed approach proves to be effective and outperforms existing methods when applied to computer science academic teams.
Matching algorithms : fundamentals, applications and challenges
- Ren, Jing, Xia, Feng, Chen, Xiangtai, Liu, Jiaying, Sultanova, Nargiz
- Authors: Ren, Jing , Xia, Feng , Chen, Xiangtai , Liu, Jiaying , Sultanova, Nargiz
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 5, no. 3 (2021), p. 332-350
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- Description: Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching. © 2017 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren, Xia Feng, Nargiz Sultanova" is provided in this record**
- Authors: Ren, Jing , Xia, Feng , Chen, Xiangtai , Liu, Jiaying , Sultanova, Nargiz
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 5, no. 3 (2021), p. 332-350
- Full Text:
- Reviewed:
- Description: Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching. © 2017 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren, Xia Feng, Nargiz Sultanova" is provided in this record**
Random walks : a review of algorithms and applications
- Xia, Feng, Liu, Jiaying, Nie, Hansong, Fu, Yonghao, Wan, Liangtian, Kong, Xiangjie
- Authors: Xia, Feng , Liu, Jiaying , Nie, Hansong , Fu, Yonghao , Wan, Liangtian , Kong, Xiangjie
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 4, no. 2 (2020), p. 95-107
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- Description: A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together. © 2017 IEEE.
- Authors: Xia, Feng , Liu, Jiaying , Nie, Hansong , Fu, Yonghao , Wan, Liangtian , Kong, Xiangjie
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 4, no. 2 (2020), p. 95-107
- Full Text:
- Reviewed:
- Description: A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together. © 2017 IEEE.
Video coding using arbitrarily shaped block partitions in globally optimal perspective
- Paul, Manoranjan, Murshed, Manzur
- Authors: Paul, Manoranjan , Murshed, Manzur
- Date: 2011
- Type: Text , Journal article
- Relation: EURASIP Journal on Advances in Signal Processing Vol. 16, no. (2011), p.
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- Description: Algorithms using content-based patterns to segment moving regions at the macroblock (MB) level have exhibited good potential for improved coding efficiency when embedded into the H.264 standard as an extra mode. The content-based pattern generation (CPG) algorithm provides local optimal result as only one pattern can be optimally generated from a given set of moving regions. But, it failed to provide optimal results for multiple patterns from entire sets. Obviously, a global optimal solution for clustering the set and then generation of multiple patterns enhances the performance farther. But a global optimal solution is not achievable due to the non-polynomial nature of the clustering problem. In this paper, we propose a near-optimal content-based pattern generation (OCPG) algorithm which outperforms the existing approach. Coupling OCPG, generating a set of patterns after clustering the MBs into several disjoint sets, with a direct pattern selection algorithm by allowing all the MBs in multiple pattern modes outperforms the existing pattern-based coding when embedded into the H.264.
- Authors: Paul, Manoranjan , Murshed, Manzur
- Date: 2011
- Type: Text , Journal article
- Relation: EURASIP Journal on Advances in Signal Processing Vol. 16, no. (2011), p.
- Full Text:
- Reviewed:
- Description: Algorithms using content-based patterns to segment moving regions at the macroblock (MB) level have exhibited good potential for improved coding efficiency when embedded into the H.264 standard as an extra mode. The content-based pattern generation (CPG) algorithm provides local optimal result as only one pattern can be optimally generated from a given set of moving regions. But, it failed to provide optimal results for multiple patterns from entire sets. Obviously, a global optimal solution for clustering the set and then generation of multiple patterns enhances the performance farther. But a global optimal solution is not achievable due to the non-polynomial nature of the clustering problem. In this paper, we propose a near-optimal content-based pattern generation (OCPG) algorithm which outperforms the existing approach. Coupling OCPG, generating a set of patterns after clustering the MBs into several disjoint sets, with a direct pattern selection algorithm by allowing all the MBs in multiple pattern modes outperforms the existing pattern-based coding when embedded into the H.264.
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