Titlebook: Knowledge Management and Acquisition for Intelligent Systems; 20th Principle and P Shiqing Wu,Xing Su,Byeong Ho Kang Conference proceedings

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Efficient Redundancy Elimination to Discovering Concise Prevalent Co-location Patterns,s among spatial features. Despite its importance, traditional frameworks for co-location pattern mining often suffer from the generation of an exponential number of patterns, many of which are redundant or insignificant. This proliferation of patterns poses significant challenges for practical appli

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,EBcGAN: An Edge-Based Conditional Generative Adversarial Network for?Image Fusion,ture details. Recent fusion techniques, including deep learning methods like auto-encoders, convolutional neural networks, and generative adversarial networks, have made significant strides but still face challenges such as inadequate texture and thermal detail capture, noise, and contamination. To

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,A Variational Approach to?Personalized Federated Learning and?Its Improvement,wever, training one standard model is not optimal for local optimization due to heterogeneity. Thus, personalized federated learning (PFL) paradigms aim to combine local and global information while preserving diversity among local clients. However, these methods require more theoretical motivation.

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,Natural Language Integration for?Multimodal Few-Shot Class-Incremental Learning: Image Classificati and 2. the catastrophic forgetting of formerly learned old classes caused by a limitation to reuse the samples of former training. To solve these problems in image classification task, we proposed improving the input feature of the classification layer by integrating a visual-semantic network for p

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,Multi-target Contrastive Objective for?Learning Property-Aware Vision-Language Representation,ve for representation learning, it typically binds one image to one text caption, limiting the ability to capture individual properties from the text. Diagnostic datasets and benchmarks are vital for developing and understanding multi-property vision-language representations but are currently undere

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,Low Cost Active Learning Framework for?Short Answer Scoring, it is necessary to generate training data for each prompt, which is time-consuming and labor-intensive. We propose a human-in-the-loop framework that achieves SAS with a minimal amount of training data through active data creation and efficient training. We fine-tuned the Sentence BERT model with a

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