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Davide Di Monda - IEEE Xplore Author Profile

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Author details

Davide Di Monda

Publications

6

Citations

21

Publications by Year

20222025

Co-Authors:

Giuseppe AcetoGiampaolo BovenziFrancesco CerasuoloDomenico CiuonzoMatteo De Ieso

Show All Co-Authors (10)

Davide Di Monda

Affiliation

DIETI and IMT School for Advanced Studies, Lucca, Italy

Publication Topics

Few-shot Learning,
Intrusion Detection,
Support Set,
Query Set,
Silhouette Score,
Transfer Learning,
Transfer Learning Approach,
Anomaly Detection,
Class Of Attacks,
Classification Task,
Computer Vision,
Distributed Denial Of Service

Biography

Davide Di Monda received the M.S. degree (summa cum laude) in computer engineering from the University of Napoli Federico II in July 2022. He is currently pursuing the Ph.D. degree with the IMT School for Advanced Studies Lucca and the University of Napoli Federico II. His research interests include cybersecurity, attack classification, and blockchain.(Based on document published on 16 October 2024).

Publications

6

Citations

21

Publications by Year

20222025

Co-Authors:

Giuseppe Aceto
Giampaolo Bovenzi
Francesco Cerasuolo
Domenico Ciuonzo
Matteo De Ieso

Show All Co-Authors (10)

Author's Published Works

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Showing 1-8 of 8 results

Conferences (8)

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Kehan Li(8)
Jie Chen(6)
Chang Liu(6)
Li Yuan(5)
Zesen Cheng(5)
Peng Cheng Laboratory, Shenzhen, China(6)
School of Electronic and Computer Engineering, Peking University, Shenzhen, China(6)
Department of Automation and BNRist, Tsinghua University, Beijing, China(5)
AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China(3)
AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzhen, China(2)
2023 IEEE/CVF International Conference on Computer Vision (ICCV)(3)
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(2)
2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI)(1)
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)(1)
2024 International Conference on Intelligent Robotics and Automatic Control (IRAC)(1)
IEEE(8)
Media(2)
Paris, France(3)
Vancouver, BC, Canada(2)
Guangzhou, China(1)
Nanjing, China(1)
Seattle, WA, USA(1)
Semantic Segmentation(4)
Vision Transformer(4)
Self-supervised Learning(3)
Image Segmentation(2)
Instance Segmentation(2)

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Results

Communication Adaptive Multi-Task Coupled Multi-Agent Reinforcement Learning Framework

Heng You;Xin Tang;Kehan Li;Tongqing Jing;Boyao Li;Zhenya Wang;Haidong Xie

2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI)

Year: 2024 | Conference Paper |

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Despite the impressive success achieved in various domains, multi-agent reinforcement learning (MARL) is still faced with the problem of incomplete information and how to deal with complex decision-making tasks with multi-task coupled. The information fusion algorithm ensures the consistency of the global state information by integrating local information to cope with information loss and communic...Show More

Communication Adaptive Multi-Task Coupled Multi-Agent Reinforcement Learning Framework

Heng You;Xin Tang;Kehan Li;Tongqing Jing;Boyao Li;Zhenya Wang;Haidong Xie

2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI)

Year: 2024 | Conference Paper |

An Adaptive Direct-Discretized RNN With Fuzzy Factor for Handling Future Distinct-Layer Inequality and Equation System Applied to Robot Manipulator Control

Binbin Qiu;Kehan Li;Zihang Li;Jinjin Guo

2024 International Conference on Intelligent Robotics and Automatic Control (IRAC)

Year: 2024 | Conference Paper |

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This paper proposes an adaptive direct-discretized recurrent neural network (ADD-RNN) algorithm with fuzzy factor to address the problem of future distinct-layer inequality and equation system (FDLIES). For comparison purposes, a traditional indirect-discretized RNN (TID-RNN) algorithm is also developed to solve the same problem. The superior performance of the proposed ADD-RNN algorithm is valida...Show More

An Adaptive Direct-Discretized RNN With Fuzzy Factor for Handling Future Distinct-Layer Inequality and Equation System Applied to Robot Manipulator Control

Binbin Qiu;Kehan Li;Zihang Li;Jinjin Guo

2024 International Conference on Intelligent Robotics and Automatic Control (IRAC)

Year: 2024 | Conference Paper |

GraCo: Granularity-Controllable Interactive Segmentation

Yian Zhao;Kehan Li;Zesen Cheng;Pengchong Qiao;Xiawu Zheng;Rongrong Ji;Chang Liu;Li Yuan;Jie Chen

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Cited by: Papers (2)

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Interactive Segmentation (IS) segments specific objects or parts in the image according to user input. Current IS pipelines fall into two categories: single-granularity out-put and multi-granularity output. The latter aims to allevi-ate the spatial ambiguity present in the former. However, the multi-granularity output pipeline suffers from limited interaction flexibility and produces redundant res...Show More

GraCo: Granularity-Controllable Interactive Segmentation

Yian Zhao;Kehan Li;Zesen Cheng;Pengchong Qiao;Xiawu Zheng;Rongrong Ji;Chang Liu;Li Yuan;Jie Chen

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2024 | Conference Paper |

Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation

Kehan Li;Yian Zhao;Zhennan Wang;Zesen Cheng;Peng Jin;Xiangyang Ji;Li Yuan;Chang Liu;Jie Chen

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

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Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis. Typically, massive and expansive pixel-level annotations are spent to train deep models by object-oriented interactions with manually labeled object masks. In this work, we reveal that informative in...Show More

Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation

Kehan Li;Yian Zhao;Zhennan Wang;Zesen Cheng;Peng Jin;Xiangyang Ji;Li Yuan;Chang Liu;Jie Chen

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

LaPE: Layer-adaptive Position Embedding for Vision Transformers with Independent Layer Normalization

Runyi Yu;Zhennan Wang;Yinhuai Wang;Kehan Li;Chang Liu;Haoyi Duan;Xiangyang Ji;Jie Chen

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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Position information is critical for Vision Transformers (VTs) due to the permutation-invariance of self-attention operations. A typical way to introduce position information is adding the absolute Position Embedding (PE) to patch embedding before entering VTs. However, this approach operates the same Layer Normalization (LN) to token embedding and PE, and delivers the same PE to each layer. This ...Show More

LaPE: Layer-adaptive Position Embedding for Vision Transformers with Independent Layer Normalization

Runyi Yu;Zhennan Wang;Yinhuai Wang;Kehan Li;Chang Liu;Haoyi Duan;Xiangyang Ji;Jie Chen

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

DiffusionRet: Generative Text-Video Retrieval with Diffusion Model

Peng Jin;Hao Li;Zesen Cheng;Kehan Li;Xiangyang Ji;Chang Liu;Li Yuan;Jie Chen

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

Cited by: Papers (19)

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Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i.e., p(candidates|query). While straightforward, this de facto paradigm overlooks the underlying data distribution p(query), which makes it challenging to identify out-of-distribution data. To address this limitation, we creatively tackle this task from a generative viewp...Show More

DiffusionRet: Generative Text-Video Retrieval with Diffusion Model

Peng Jin;Hao Li;Zesen Cheng;Kehan Li;Xiangyang Ji;Chang Liu;Li Yuan;Jie Chen

2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Year: 2023 | Conference Paper |

ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation

Kehan Li;Zhennan Wang;Zesen Cheng;Runyi Yu;Yian Zhao;Guoli Song;Chang Liu;Li Yuan;Jie Chen

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (15)

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Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e.g., unsupervised semantic segmentation (USS). The extracted relationship among pixel-level representations typically contains rich class-aware information that semantically ide...Show More

ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation

Kehan Li;Zhennan Wang;Zesen Cheng;Runyi Yu;Yian Zhao;Guoli Song;Chang Liu;Li Yuan;Jie Chen

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation

Zesen Cheng;Pengchong Qiao;Kehan Li;Siheng Li;Pengxu Wei;Xiangyang Ji;Li Yuan;Chang Liu;Jie Chen

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (26)

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Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted. Although tremendous efforts have been made to recall precise and complete locations for each class, existing methods still commonly suffer from the unsolicited Out-of-Candidate (OC) error predictions that do not belong to the label candi...Show More

Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation

Zesen Cheng;Pengchong Qiao;Kehan Li;Siheng Li;Pengxu Wei;Xiangyang Ji;Li Yuan;Chang Liu;Jie Chen

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

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