The scene text retrieval system can search all the images containing the query text in the gallery based on the input query text and locate the position of the query text at the same time. The current state-...
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Numerous fields employ correlation-based searches to provide insights about relationships in a database. Herein we propose a correlated DNA subsequence search for DNA databases to extract DNA subsequences that frequen...
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The channel attention mechanism and spatial attention mechanism are crucial in enhancing the performance of convolutional neural networks. However, most existing methods focus on developing more intricate attention mo...
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ISBN:
(数字)9789819756001
ISBN:
(纸本)9789819755998;9789819756001
The channel attention mechanism and spatial attention mechanism are crucial in enhancing the performance of convolutional neural networks. However, most existing methods focus on developing more intricate attention modules to improve performance, which inevitably increases the number of model parameters. To address the trade-off between performance and parameter count, this paper introduces an efficient Parameter-free Attention Aggregation Model (PAAM) plug-and-play module. The module first creates a Local Feature Enhancement Module (LFEM) using adaptive pooling. Firstly, the local feature enhancement module (LFEM) is constructed through adaptive pooling to enhance the expression of local features;secondly, the local-global feature interaction module (L-GFIM) is used to realize the mutual compensation between local and global features, which effectively extends the coverage of local-global interaction. The experimental results indicate that PAAM outperforms the SOTA model in ImageNet-1K, Cifar-10, and Cifar-100 image classification datasets.
Due to various factors, some parts of images can be lost. Recovering the damaged regions of images is essential. In this paper, a single image inpainting method using Wasserstein Generative Adversarial Networks (WGAN)...
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The study of nonlinear problems associated with the process of heat transfer in material is very important for practice. Previously, the authors proposed an effective algorithm for determining the volumetric heat capa...
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The discovery of frequent generators of high utility itemsets (FGHUIs) holds great importance as they provide concise representations of frequent high utility itemsets (FHUIs). FGHUIs are crucial for generating nonred...
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Clinical texts encompass a wide range of information such as patient’s history, disease diagnosis and prescribed drugs, reflecting details and nuances that are valuable in providing knowledge. In the present study, a...
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Effective fusion of RGB-D multi-modal features is crucial for RGB-D object tracking. Existing fusion methods mainly guide the interaction of RGB and depth by dense attention, but such formulation relies only...
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Epistemic logic can be used to reason about statements such as 'I know that you know that I know that.'. In this logic, and its extensions, it is commonly assumed that agents can reason about epistemic stateme...
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ISBN:
(数字)9783031517778
ISBN:
(纸本)9783031517761;9783031517778
Epistemic logic can be used to reason about statements such as 'I know that you know that I know that.'. In this logic, and its extensions, it is commonly assumed that agents can reason about epistemic statements of arbitrary nesting depth. In contrast, empirical findings on Theory of Mind, the ability to (recursively) reason about mental states of others, show that human recursive reasoning capability has an upper bound. In the present paper we work towards resolving this disparity by proposing some elements of a logic of bounded Theory of Mind, built on Public Announcement Logic. Using this logic, and a statistical method called Random-Effects Bayesian Model Selection, we estimate the distribution of Theory of Mind levels in the participant population of a previous behavioral experiment. Despite not modeling stochastic behavior, we find that approximately three-quarters of participants' decisions can be described using Theory of Mind. In contrast to previous empirical research, our models estimate the majority of participants to be second-order Theory of Mind users.
In this paper, we introduce a new type of bionic AI that enhances decision-making unpredictability by incorporating responses from a living fly. Traditional AI systems, while reliable and predictable, lack nuanced and...
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