Fine-tuning is an effective technique to enhance network performance in scenarios with limited labeled data. To achieve this, recent methods exploit the knowledge mined in the source model (e.g., feature maps) to cons...
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The effective extraction of spatial-angular features plays a crucial role in light field image super-resolution (LFSR) tasks, and the introduction of convolution and Transformers leads to significant improvement in th...
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Obesity is a condition where there is excess fat in the body, and it is one of the world's most extreme and dangerous dietary diseases. Genetic factors, lack of physical activity, unhealthy eating patterns, or a c...
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The advent of Industry 4.0 and the Industrial Internet of Things (IIoT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies ...
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Emotion recognition techniques in current literature tend to follow a centralized training process that puts user privacy at risk. Federated Learning (FL) can fill these gaps by utilizing the principles of iterative d...
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Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving soft...
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Deep learning has played an important role in many real-life applications, especially in image classification. It is often found that some domain data are highly skewed, i.e., most of the data belongs to a handful of ...
Deep learning has played an important role in many real-life applications, especially in image classification. It is often found that some domain data are highly skewed, i.e., most of the data belongs to a handful of majority classes, and the minority classes only contain small amounts of information. It is important to acknowledge that skewed class distribution poses a significant challenge to machine learning algorithms. Due to which in case of imbalanced data distribution, the majority of machine and deep learning algorithms are not effective or may fail when it is highly imbalanced. In this study, a comprehensive analysis in case of imbalanced dataset is performed by considering deep learning based well known models. In particular, the best feature extractor model is identified and the current trend of latest feature extraction model is investigated. Moreover, to determine the global scientific research on the image classification of imbalanced mushroom dataset, a bibliometric analysis is conducted from 1991 to 2022. In summary, our findings may offer researchers a quick benchmarking reference and alternative approach to assessing trends in imbalanced data distributions in image classification research.
In traffic management, accurate forecasting of short-term traffic patterns is of utmost importance to achieve optimal performance and efficiency of road networks. This research proposes a prediction technique for shor...
In traffic management, accurate forecasting of short-term traffic patterns is of utmost importance to achieve optimal performance and efficiency of road networks. This research proposes a prediction technique for short-term traffic flow, which utilizes empirical modal decomposition (EMD) and long short-term memory neural networks (LSTM). Firstly, the traffic flow sequence is decomposed into a series of relatively stable subseries using EMD, minimizing the impact of various trend data interactions. Secondly, to improve model training efficiency, normalization is applied separately to each subseries. Subsequently, an LSTM-based time-series prediction model is built for each subseries, which enhances the model's predictive accuracy. Finally, the forecasted values of short-term traffic flow are obtained by aggregating the prediction outcomes of each subseries. The simulation results demonstrate that the proposed method more accurately predicts the traffic flow change trend and achieves higher stability than conventional prediction techniques.
Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devices. One major challenge preventing th...
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Full-reference image quality assessment (FR-IQA) models generally operate by measuring the visual differences between a degraded image and its reference. However, existing FR-IQA models including both the classical on...
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ISBN:
(数字)9798350390155
ISBN:
(纸本)9798350390162
Full-reference image quality assessment (FR-IQA) models generally operate by measuring the visual differences between a degraded image and its reference. However, existing FR-IQA models including both the classical ones (e.g., PSNR and SSIM) and deep-learning based measures (e.g., LPIPS and DISTS) still exhibit limitations in capturing the full perception characteristics of the human visual system (HVS). In this paper, instead of designing a new FR-IQA measure, we aim to explore a generalized human visual attention estimation strategy to mimic the process of human quality rating and enhance existing IQA models. In particular, we model human attention generation by measuring the statistical dependency between the degraded image and the reference image. The dependency is captured in a training-free manner by our proposed sliced maximal information coefficient and exhibits surprising generalization in different IQA measures. Experimental results verify the performance of existing IQA models can be consistently improved when our attention module is incorporated. The source code is available at https://***/KANGX99/SMIC.
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