An effective knowledge learning strategy combined with a lightweight network architecture is crucial for the practical deployment of multi-modal image fusion. While existing methods have made significant progress in the visual perception of fused results, their model complexity and generalization capabilities still require further optimization. In this paper, we propose a novel passive-active distillation learning framework for multi-modal image fusion, termed DSKFuse, which integrates the Dynamic Sparse Transformer and the latent Kolmogorov-Arnold Network (KAN). Specifically, we design an efficient fusion architecture trained via a two-stage knowledge distillation strategy, seamlessly integrating passive and active learning methodologies. In the first stage, passive distillation learning enhances the fusion network by extracting valuable knowledge from complex fusion models. In the second stage, an active knowledge distillation approach is implemented, enabling the model to autonomously capture discriminative features from source images, thereby improving the robustness and generalization of DSKFuse. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance in both image fusion and downstream tasks, including detection and segmentation. The code will be released at https://github.com/DZSYUNNAN/DSKFuse .

A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest, which is different from traditional value decomposition or communication mechanisms. It enables agents to understand and anticipate others' behaviors and facilitates their collaboration. Inspired by recent research on the legibility that allows agents to reveal their intentions through their behavior, we propose a multiagent active legibility framework to improve their performance. The legibility-oriented framework drives agents to conduct legible actions so as to help others optimise their behaviors. In addition, we design a series of problem domains that emulate a common legibility-needed scenario and effectively characterize the legibility in multiagent reinforcement learning. The experimental results demonstrate that the new framework is more efficient and requires less training time compared to several multiagent reinforcement learning algorithms.

Medical image fusion aims to integrate multi-sensor source images into a unified representation, providing comprehensive and diagnostically enriched information to support clinical decision-making. However, the scarcity of labeled data presents significant challenges in effectively learning complementary features across modalities. In this paper, we propose a novel attribute-aware contrastive learning network, called ACL-Net, boosting medical image fusion performance. Specifically, we introduce the attribute transformation strategy to simulate variations in pixel intensity and structural patterns, guiding the model to focus on critical cross-modal information. In this way, it enhances contrastive learning by generating diverse negative pairs, thereby mitigating the scarcity of negative samples in unsupervised fusion scenarios. Extensive experiments demonstrate that our method achieves superior performance compared to state-of-the-art medical image fusion methods.

Accurate pathological image segmentation is crucial for the clinical diagnosis of breast cancer. However, existing methods of pathological segmentation face challenges due to the variability and complexity of breast cancer on pathological images. To address these issues, we propose a novel segmentaion network called template-matching pathological segmentation network. Our method incorporates an innovative template matching strategy inspired by the diagnostic process of pathologists. The template matching strategy is to utilize visual transformer to establish a correlative relationship between cancer lesions and corresponding templates. To improve feature utilization of pathological images, PSVTNet introduces detailed information attention and information entropy attention. Detailed information attention aims to exploit detailed information by serving as the path connecting shallow-layer and deep-layer features. Meanwhile, information entropy attention can redistribute feature weights to high-entropy regions according to the information-entropy attention map. Additionally, this work releases a comprehensive pathological dataset that comprises labeled pathological images. These images are collected from breast and stomach cancers with hematoxylin&eosin and human epidermal growth factor receptor-2 staining. Extensive experiments demonstrate that PSVTNet significantly outperforms state-of-the-art methods on pathologic images of breast cancer, but can also process pathologic images of stomach cancer carrying with same diagnosed features as the breast cancer.

This paper investigates a novel client selection for the volatile Federated Learning (FL) systems, where volatility means that the state of the client set, client datasets, and client training status will change over time. We study how to select clients dynamically to mitigate the volatility. Particularly, the volatile client selection problem is formulated as a classification problem, and we propose two new metric features. The Multi-Feature Volatile Client Selection (MFVCS) algorithm, which considers client training capacity, client-weighted data quality, and client historical selection entropy, is proposed to solve the volatile client selection problem. Moreover, we have developed an adaptive dynamic weighting algorithm that allows for dynamic updating of the weight for each feature. We propose a volatility ratio to measure client volatility. The experimental results indicate that the proposed algorithm demonstrates strong robustness and better performance under different volatility ratios of the client set. In particular, the proposed MFVCS algorithm improves the model accuracy at most by $\mathbf{9.2\%}, \mathbf{9.6\%}$ and $\mathbf{12.5\%}$ under 0.01 volatility ratio, 0.05 volatility ratio and 0.1 volatility ratio, respectively.

Medical image fusion technology is an essential component of computer-aided diagnosis, which aims to extract useful cross-modality cues from raw signals to generate high-quality fused images. Many advanced methods focus on designing fusion rules, but there is still room for improvement in cross-modal information extraction. To this end, we propose a novel encoder-decoder architecture with three technical novelties. First, we divide the medical images into two attributes, namely pixel intensity distribution attributes and texture attributes, and thus design two self-reconstruction tasks to mine as many specific features as possible. Second, we propose a hybrid network combining a CNN and a transformer module to model both long-range and short-range dependencies. Moreover, we construct a self-adaptive weight fusion rule that automatically measures salient features. Extensive experiments on a public medical image dataset and other multimodal datasets show that the proposed method achieves satisfactory performance.

In order to enhance the filtering of spam on the Internet and improve the experience of Internet users, this paper proposed to convert the email text into vector features using the vector space model, constructed a two-dimensional matrix, and used a convolutional neural network (CNN) to identify spam on the Internet. The CNN was compared with other two classifiers, support vector machine (SVM), and backward-propagation neural network (BPNN), in simulation experiments. The final results showed that the spam recognition algorithm with CNN as the classifier had better recognition performance than the algorithms with SVM and BPNN classifiers and was also more advantageous in terms of recognition cost and time for spam; in addition, the CNN had the best recognition performance when the number of extracted features was 15.

Abstract Due to different lighting environments and equipment limitations, low‐light images have high noise, low contrast and unobvious colours. The main purpose of low‐light image enhancement is to preserve the details and suppress noise as much as possible while improving the contrast of the image. Here, different networks are first combined to construct a multi‐branch module for features extraction, and use the module and Retinex theory to extract the reflection map of the image. Then an attention mechanism is introduced into the multi‐branch construction to balance the feature weight of each branch, and get the final result by the reconstruction module. The Retinex theory is used to calculate the L 1 loss and the gradient loss for the intermediate feature map of the entire model to train our framework. The entire process is completed in an end‐to‐end‐way, which avoids the hand‐crafted reconstruction rules and reduces the workload. What's more, a large number of experiments demonstrate that the proposed framework performs better results than state‐of‐the‐art algorithms in both quantitative and qualitative evaluations of image enhancement.

This study proposes a fusion method for high dynamic range images based on gradient information transformation. In the proposed work, the authors first measure the three exposure weights of the source images, namely, local contrast, luminance and spatial structure. Then, the exposure weights are merged through a multi‐scale Laplacian pyramid scheme. For the weight maps measurement, the dense scale‐invariant feature transform method is used to calculate the local contrast around each pixel location, rather than a single pixel. The image luminance levels are computed in the gradient domain to get more visual information and the authors leverage the dictionary learning to effectively extract the luminance of images. Additionally, to better preserve the spatial structure of the source images, the just‐noticeable‐distortion technique is employed. By comparing the experimental results both subjectively and objectively, it is evident that the proposed method represents an improvement over some exciting methods.