In recent years, deep learning has revolutionized hyperspectral image (HSI) classification. However, it remains a significant challenge to achieve high-precision classification with limited image quality and labeled samples. Most existing methods fail to effectively leverage unlabeled samples and neglect the impact of image quality degradation on classification performance. To address these issues, this paper proposes a Fusion-Driven Task Mutual-Guidance Network (FTMNet), which enhances image quality and improves classification performance through mutual guidance between fusion and classification tasks. Specifically, we propose an image fusion subnet integrating contrastive learning to jointly optimize input quality enhancement and discriminative feature representation through multi-objective constraints. To mitigate sample scarcity, a multi-task interactive multimodal contrastive architecture is developed, leveraging cross-modal complementarity and cross-task feature sharing mechanisms to strengthen discriminative power. Furthermore, we introduce a cross-task collaborative mutual-guidance strategy that synchronizes inter-task information exchange via learnable parametric constraints, forming unified optimization directions for coordinated performance enhancement. The experimental results demonstrate that the proposed method outperforms the existing state-of-the-art methods in both quantitative and qualitative aspects. Code is available at https://github.com/Jiahuiqu/FTMNet.

In the domain of medical diagnostics, precise identification of various skin and oral diseases is vital for effective patient care. In particular, Mpox is a potentially dangerous viral disease with zoonotic origins, capable of human-to-human transmission, underscoring the urgency of precise diagnostic methods for timely intervention. This paper introduces a novel approach named the Choquet Fuzzy Integral-based Ensemble (CFI-Net) for accurate classification of skin diseases, with a specific emphasis on detecting Mpox, foot ulcers, and various mouth and oral diseases. Our methodology begins with Transfer Learning, enhancing the classification capabilities of base classifiers (DenseNet169, MobileNetV1 and DenseNet201) by incorporating additional layers. Subsequently, we aggregate the prediction scores from each base classifier using the Choquet fuzzy integral (CFI) to derive the final predicted labels, thus ensuring dynamic and robust predictions. Fuzzy measures, a crucial component of this fuzzy integral-based ensemble method, are typically determined through manual experimentation in previous approaches. However, in our study, we have tackled the challenge of manual tuning by employing meta-heuristic optimization algorithm to precisely configure the fuzzy measures for optimal performance. A rigorous evaluation is conducted on four publicly available datasets, encompassing two Mpox datasets, a foot ulcer dataset, and a mouth and oral disease dataset. The experiments reveal the remarkable effectiveness of CFI-Net in significantly improving disease classification accuracy. Additionally, we employ Grad-CAM analysis to provide insights into the decision-making processes of our models. Our findings underscore the exceptional performance of CFI-Net, achieving accuracy rates of 98.06% and 94.81% for Mpox detection, 99.06% for foot ulcer detection, and an impressive 99.61% for mouth and oral disease classification. This research not only contributes to the advancement of disease diagnosis but also demonstrates the effectiveness of ensemble learning techniques coupled with fuzzy integral-based fusion in enhancing diagnostic accuracy.

Snow images usually contain snow grains, snow streaks, and mist, which greatly affect the visibility of images. Currently, supervised learning with synthetic data often faces limitations when it comes to handling real-world snow images. To address this crucial issue, this work proposes an unsupervised domain adaptation image snow removal framework. The framework improves the performance on real-world images by learning a domain classifier in adversarial training manner. Additionally, considering the diversity of snowflake shapes and sizes in real-world snow images, we design a multiple-kernel dilated convolution module. Extensive experiments on three representative datasets have validated that our model can achieve better results than existing desnowing methods. More importantly, experiments on real datasets show that the proposed method obtains state-of-the-art performance in real-world desnowing.

In recent years, saliency object detection methods based on convolutional neural networks have been widely studied, and have achieved excellent performance in clear images. However, due to the low visibility of images in foggy conditions, the existing saliency object detection methods will be seriously affected or even ineffective. To address this problem, we introduce an end-to-end multi-task learning network. We design two subetworks for depth estimation and image restoration as auxiliary tasks to improve saliency object detection in foggy conditions. According to different characteristics of vision tasks, different shared layers are assigned to improve the performance of saliency object detection. Experiments show that our method has been greatly improved on both synthetic foggy datasets and real-to-world foggy datasets, outperforming many state-to-the-art saliency object detection methods.

Anomaly detection has been extensively implemented in industry. The reality is that an application may have numerous scenarios where anomalies need to be monitored. However, the complete process of anomaly detection will take much time, including data acquisition, data processing, model training, and model deployment. In particular, some simple scenarios do not require building complex anomaly detection models. This results in a waste of resources. To solve these problems, we build an anomaly detection pipeline(ADOps) to modularize each step. For simple anomaly detection scenarios, no programming is required and new anomaly detection tasks can be created by simply modifying the configuration file. In addition, it can also improve the development efficiency of complex anomaly detection models. We show how users create anomaly detection tasks on the anomaly detection pipeline and how engineers use it to develop anomaly detection models.

In the face of intensive computing tasks with massive data, cloud computing is difficult to provide high-quality services. Edge computing extends cloud services to the edge of the network by introducing edge devices between terminal devices and the cloud. For limited edge server resources, it is especially important to optimize offload strategies by accurately predicting the load on the terminal device. This paper proposes a C-LSTM prediction model based on deep neural network to predict the CPU utilization of terminal equipment in the future, and then proposes a distributed greedy algorithm for offloading decision. The simulation results show that the accuracy of C-LSTM prediction model is higher than other baseline models, reduces energy consumption and delay, and provides high-quality computing services.

Single image super-resolution (SISR) is a challenging ill-posed problem due to multiple high-resolution (HR) images can degenerate into the same low-resolution (LR) image. However, existing deep learning-based super-resolution (SR) methods always have blurred edge structures in the restored images. In addition, they mainly build more profound and more complex convolutional neural networks (CNN), which leads to substantial computational overhead. To address these issues, we propose the feature fusion super-resolution network (FFSRN) that uses the gradient map of the image to guide the restoration. In FFSRN, we propose the split and shuffle concat block (SSCB), which can extract rich features while controlling the model size and computational effort. We also introduce gradient branching to provide additional structural priors for the reconstruction process to restore high-resolution gradient mapping. Experimental results show that this method has a better peak signal-to-noise ratio, computational overhead and visual quality than the existing super-resolution algorithms. Code is available at https://github.com/Qyzs/FFSRN.