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Progressive Neural Architecture Generation with Weaker Predictors 31st

Progressive Neural Architecture Generation with Weaker Pred...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Zhang, Zhengzhuo Zhuang, Liansheng University of Science and Technology of China Hefei China

ISBN: (纸本)9789819620630

Neural Architecture Search (NAS) methods often suffer from low search efficiency since they have to explore a large and complex architecture search space. To accelerate the architecture search, generative methods learn a search space capturing intricate architecture distributions and generate promising architectures guided by a strong predictor within the latent space. However, since the architecture space is often exponentially large and highly non-convex, even a very strong predictor model is difficult in fitting the whole space, which may degrade their performance. To address this problem, this paper proposes a novel framework named Progressive Neural Architecture Generation with Weaker Predictors (WeakPNAG), which uses conditional diffusion models to generate promising architectures guided by weak predictors. Different from existing generative NAS methods which use a single strong predictor, our WeakPNAG progressively shrinks the sample space based on predictions from previous weak predictors, and updates new weak predictors towards the subspace of better architectures. In this way, our WeakPNAG iteratively learns to generate samples from increasingly promising latent subspaces. Extensive experiments on standard benchmarks demonstrate that our WeakPNAG achieves superior performance with reduced evaluation time compared with SOTA NAS methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Sparse Context Transformer for Few-Shot Object Detection 21st

Sparse Context Transformer for Few-Shot Object Detection

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Jiu, Mingyuan Mei, Jie Sahbi, Hichem Jiang, Xiaoheng Xu, Mingliang School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou University Zhengzhou China National SuperComputing Center in Zhengzhou Zhengzhou China Sorbonne University CNRS LIP6 Paris75005 France

ISBN: (纸本)9789819601240

Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a source domain prior to its fine-tuning in a target domain. However, it is challenging for the fine-tuned models to effectively identify new classes in the target domain, particularly when the underlying labeled training data are scarce. In this paper, we devise a novel sparse context transformer (SCT) that effectively leverages object knowledge in the source domain, and automatically learns a sparse context from only few training images in the target domain. As a result, it combines different relevant clues in order to enhance the discrimination power of the learned detectors and reduce class confusion. We evaluated the proposed method on two challenging few-shot object detection benchmarks, and empirical results show that the proposed method obtains competitive performance compared to the related state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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AutoTable: Effective and Efficient Automated Feature Transformation for Tabular Data 25th

AutoTable: Effective and Efficient Automated Feature Transf...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Huang, Shanshan Zhu, Junpeng Zhang, Fengyan Cai, Peng Dong, Qiwen School of Data Science and Engineering East China Normal University No. 3663 Zhongshan North Road Shanghai200062 China

ISBN: (纸本)9789819605781

Mission-critical data are commonly organized as tables within relational databases, and feature transformation from these tabular data is a pivotal component of the machine learning pipeline for business intelligence. However, automating this process poses a significant challenge, primarily due to the exponential growth in the size of the search space with an increase in the number of features (i.e., columns or dimensions of the table) and transform functions. This paper presents AutoTable, an effective and efficient feature transformation framework for tabular data using reinforcement learning. Specifically, AutoTable formulates the feature transformation problem as a search process carried out on a transformation tree, which offers a more well-structured search space and facilitates fine-grained exploration, empowering domain experts to perform AFT tasks with minimal statistical and machine learning expertise. To further improve search performance, we propose merging and lazy loading mechanisms. Experimental results demonstrate that AutoTable outperforms state-of-the-art approaches in terms of both efficiency and effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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A BERT-Based Method of Named Entity Recognition for Ukiyo-e Titles 26th

A BERT-Based Method of Named Entity Recognition for Ukiyo-...

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26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024

作者： Wu, Bohao Maeda, Akira Graduate School of Information Science and Engineering Ritsumeikan University Osaka Ibaraki567-8570 Japan College of Information Science and Engineering Ritsumeikan University Osaka Ibaraki567-8570 Japan

ISBN: (纸本)9789819608645

Named entity recognition (NER) is a particularly challenging task, especially for historical documents that lack extensive annotated datasets [6, 14]. The titles of ukiyo-e, a genre of Japanese artworks, contain a significant number of entities and are composed of short texts rich in historical information. The complexity and brevity of these titles pose considerable challenges for analysis. This paper presents the construction of an ukiyo-e NER dataset and introduces a BERT-based NER methodology that achieves notable success in entity recognition within ukiyo-e titles. The study underscores the effectiveness of BERT and its derivative models on the ukiyo-e NER dataset, proposing a viable NER solution for historical documents. The proposed approach demonstrates the capability to perform NER tasks on ukiyo-e titles using pre-trained models on a relatively small annotated dataset, achieving an accuracy exceeding 80%. Furthermore, this paper examines the distinct characteristics of BERT and its derivative models, optimizing their application to the ukiyo-e NER dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： BERT Named entity recognition Ukiyo-e

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Research on Tropical Cyclone Tracks Classification Methods and Feature Analysis in the Western Northern Pacific 24th

Research on Tropical Cyclone Tracks Classification Methods a...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xu, Nan Yang, Baisong Deng, Lihao Ren, Jia The Institute of Data Science City University of Macau Macau China The College of Information and Communication Engineering Hainan University Haikou China

ISBN: (纸本)9789819615414

Tropical Cyclone tracks serve as crucial criteria for discerning the affected regions and the extent of impact caused by tropical cyclones. Classifying tropical cyclone tracks allows for the exploration of tropical cyclone characteristics and the assessment of the associated impacts. This paper utilized the RSMC-Tokyo dataset provided by the Japan Meteorological Agency (JMA), spanning the years 2000 to 2021. Employing the K-Means clustering algorithm, 13 variables describing both the intrinsic features of tropical cyclone tracks and their external impacts were selected as clustering indicators. The tropical cyclones in the northwestern Pacific region were classified into four categories, and a comprehensive feature analysis was conducted for each class. Results indicate significant differences in the activity range, intensity, seasonal occurrence, and landfall patterns among the identified tropical cyclone classes. Notably, tropical cyclones originating in the South China Sea exhibit characteristics such as rapid development, high intensity, unpredictability, short cycles, and a greater propensity to affect Hainan Province. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tropical cyclone

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VideoEase at VBS2025: An Interactive Video Retrieval System 31st

VideoEase at VBS2025: An Interactive Video Retrieval System

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Tran, Quang-Linh Nguyen, Binh Jones, Gareth J. F. Gurrin, Cathal ADAPT Centre School of Computing Dublin City University Dublin 9 Dublin Ireland University of Science Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819620739

We present the VideoEase interactive video retrieval system, which we used to participate in VBS2025. This is the first time that VideoEase has taken part in the VBS challenge. VideoEase is built on the Milvus vector database, which supports vector searches on massive datasets with high-dimensional vectors. The CLIP, BLIP2, and OpenCLIP models play a crucial role in encoding keyframe images and queries into embeddings. A new user interface with simple yet effective components is also introduced. We experiment with VBS24 queries to evaluate the performance of the VideoEase system. Our results show that answer rank is improved by merging outputs from several models with appropriate weights. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： User interfaces

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FleX: Interpreting Graph Neural Networks with Subgraph Extraction and Flexible Objective Estimation 30th

FleX: Interpreting Graph Neural Networks with Subgraph Extr...

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30th International Conference on Cooperative Information Systems, CoopIS 2024

作者： Nguyen, Duy Le, Thanh Le, Bac Faculty of Information Technology University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031813740

Graph Neural Networks (GNNs) have shown remarkable results in graph-related tasks, yet interpreting their decision-making process remains challenging. Most existing methods for interpreting GNNs focus on finding a subgraph that preserves the model’s predictions. However, removing edges alters the original structure, making the optimization process heavily dependent on the loss function. In this paper, we propose FleX, a novel approach that transcends these limitations by using a distillation model to estimate prediction values after subgraph extraction. Our method combines implicit and explicit edge masking techniques to identify the most relevant subgraph. We introduce a flexible loss estimation strategy that allows for a more robust optimization process. Experimental results demonstrate that FleX outperforms most existing Graph XAI models across various benchmark datasets, achieving superior performance in interpreting GNNs. This approach enhances the interpretability of GNNs while maintaining high accuracy, contributing to more trustworthy and explainable graph-based machine learning models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph explainability Graph knowledge distillation Graph neural network Subgraph extraction

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Audio-Visual Navigation with Anti-Backtracking 27th

Audio-Visual Navigation with Anti-Backtracking

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Zhenghao Tang, Hao Yan, Yan Department of Computer Science University of Illinois Chicago Chicago United States Robotics Institute Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9783031784552

Embodied navigation, which involves robotic agents exploring an unknown environment to reach target locations with egocentric observation, is a complex problem in the field of embodied AI. Audio-visual navigation extends this concept by equipping agents with both visual and auditory sensors. Recent studies have explored the audio-visual navigation task, exploring its potential and intricacies. Current methodologies, despite their achievements, often fail to fully utilize the capabilities of these sensory modalities, leading to sub-optimal designs and inefficiencies. In addition, the prevalent backtracking problem in navigation tasks often leads to redundant movements by robots. To address these challenges, we introduce the Audio-Visual Guided Navigation (AVGN) model. At its core, AVGN champions the fusion of visual and auditory data through sophisticated modality fusion layers. Our model represents a significant advancement by employing transformers for visual encoding and deploying a novel Dual Stage Feature Integration (DSFI) to decode latent interrelationships between the visual and audio realms. With AVGN, the backtracking issue is mitigated, as the acoustic map synergizes more effectively with visual features for informed action decisions. Furthermore, targeting the backtracking problem, we propose a unique set of reward structures to guide and refine the actions of the agent. Benchmark evaluations on Replica and Matterport3D datasets validate our claims, and AVGN notably surpasses existing methodologies in audio-visual navigation tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reinforcement learning

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Robust Graph Learning Against Camouflaged Malicious Actors 25th

Robust Graph Learning Against Camouflaged Malicious Actors

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Haghighi, Venus Shabani, Nasrin Soltani, Behnaz Yao, Lina Sheng, Quan Z. Yang, Jian Beheshti, Amin School of Computing Macquarie University SydneyNSW2109 Australia School of Computer Science and Engineering University of New South Wales KensingtonNSW Australia

ISBN: (纸本)9789819605668

Graph Neural Networks (GNNs) have achieved great success in various graph-related applications such as fraud detection. However, GNN-based fraud detection models suffer from the camouflage behavior of malicious actors. Camouflaged fraudsters establish many normal connections to benign entities in the network to alleviate their suspiciousness, and eventually bypass the detection systems. To tackle this problem, we propose a new Multiple Adaptive Channels Aggregation Graph Neural Network for Detecting Camouflaged Fraudsters (named MAGNET for short). First, MAGNET includes a graph-agnostic edge labeling module to generate edge labels and domination signals (i.e., homophily-domination or heterophily-domination) for a given neighborhood. Second, MAGNET leverages multiple adaptive aggregation channels to improve graph learning against camouflaged fraudsters. Third, MAGNET adopts a multi-relational combination module to obtain final representations based on different relations for a multi-relational fraud graph. We conduct extensive experiments on two real-world fraud datasets, and our results show that MAGNET outperforms the state-of-the-art baselines. The source codes and datasets of our work are available at https://***/VenusHaghighi/MAGNET. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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Towards Responsible Decisions with Limited Training Data Using Human-in-the-Loop 20th

Towards Responsible Decisions with Limited Training Data Us...

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Mahidadia, Ashesh Bain, Michael Suryanto, Hendra Kang, Byeong Guan, Charles Compton, Paul Rich Data Corporation Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia School of Information and Communication Technology University of Tasmania Hobart Australia

ISBN: (纸本)9789819600250

A fundamental issue with machine learning is that the training data must contain sufficient examples of every data pattern of interest for the essentially statistical techniques of machine learning to derive a model that accurately covers all data patterns. For example, in real-world situations, samples from minority populations may not be adequately represented in available training datasets, resulting in models that are biased in favour of majority populations. We present an approach to partially address this problem by using domain knowledge to construct rules to correctly classify, specifically, those cases representing rare patterns in training data that the machine learning model has incorrectly classified. We use Ripple-Down Rules (RDR) to add rules, as it is a proven approach to knowledge acquisition and incremental maintenance that enables rules to be very easily and quickly added to a knowledge base on a case-by-case basis. This paper extends previous work using a human expert by using both artificial and real-world data and a simulated expert to add rules in multiple controlled experiments. Results show that domain knowledge can be successfully used in our approach to mitigate performance issues arising from a limited representation of a subgroup in the available training dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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