Despite remarkable advancements in supervised pansharpening neural networks, these methods face domain adaptation challenges of resolution due to the intrinsic disparity between simulated reduced-resolution training data and real-world full-resolution scenarios. To bridge this gap, we propose an unsupervised pansharpening framework, CLIPPan, that enables model training at full resolution directly by taking CLIP, a visual-language model, as a supervisor. However, directly applying CLIP to supervise pansharpening remains challenging due to its inherent bias toward natural images and limited understanding of pansharpening tasks. Therefore, we first introduce a lightweight fine-tuning pipeline that adapts CLIP to recognize low-resolution multispectral, panchromatic, and high-resolution multispectral images, as well as to understand the pansharpening process. Then, building on the adapted CLIP, we formulate a novel loss integrating semantic language constraints, which aligns image-level fusion transitions with protocol-aligned textual prompts (e.g., Wald's or Khan's descriptions), thus enabling CLIPPan to use language as a powerful supervisory signal and guide fusion learning without ground truth. Extensive experiments demonstrate that CLIPPan consistently improves spectral and spatial fidelity across various pansharpening backbones on real-world datasets, setting a new state of the art for unsupervised full-resolution pansharpening.

With the rapid rise of large models, copyright protection for generated image content has become a critical security challenge. Although deep learning watermarking techniques offer an effective solution for digital image copyright protection, they still face limitations in terms of visual quality, robustness and generalization. To address these issues, this paper proposes an adaptive robust iterative watermarking framework (ARIW-Framework) that achieves high-quality watermarked images while maintaining exceptional robustness and generalization performance. Specifically, we introduce an iterative approach to optimize the encoder for generating robust residuals. The encoder incorporates noise layers and a decoder to compute robustness weights for residuals under various noise attacks. By employing a parallel optimization strategy, the framework enhances robustness against multiple types of noise attacks. Furthermore, we leverage image gradients to determine the embedding strength at each pixel location, significantly improving the visual quality of the watermarked images. Extensive experiments demonstrate that the proposed method achieves superior visual quality while exhibiting remarkable robustness and generalization against noise attacks.

Adaptive optimizers such as Adam and RMSProp have gained attraction in complex neural networks, including generative adversarial networks (GANs) and Transformers, thanks to their stable performance and fast convergence compared to non-adaptive optimizers. A frequently overlooked limitation of adaptive optimizers is that adjusting the learning rate of each dimension individually would ignore the knowledge of the whole loss landscape, resulting in slow updates of parameters, invalidating the learning rate adjustment strategy and eventually leading to widespread insufficient convergence of parameters. In this paper, we propose HVAdam, a novel optimizer that associates all dimensions of the parameters to find a new parameter update direction, leading to a refined parameter update strategy for an increased convergence rate. We validated HVAdam in extensive experiments, showing its faster convergence, higher accuracy, and more stable performance on image classification, image generation, and natural language processing tasks. Particularly, HVAdam achieves a significant improvement on GANs compared with other state-of-the-art methods, especially in Wasserstein-GAN (WGAN) and its improved version with gradient penalty (WGAN-GP).

In a dynamic environment, organisations are increasingly compelled to utilise big data analytics (BDA) to develop organisational resilience, which will help them weather disruptions and gain competitiveness. By investigating 213 Chinese firms across a diverse array of industries, this study seeks to uncover the mechanisms and boundary conditions through which BDA capability affects firm performance. Drawing on the organisational capabilities-based theory and information technology-enabled organisational capabilities perspective, we both theoretically and empirically verify that two types of organisational resilience (i.e., proactive resilience and reactive resilience) mediate the relationship between BDA capability and firm performance. Our findings reveal that environmental dynamism positively moderates the impact of BDA capability on reactive resilience and strengthens the positive mediation effect of reactive resilience on the relationship between BDA capability and firm performance. This study contributes to the information systems literature by theoretically and empirically clarifying the pivotal roles of proactive resilience and reactive resilience as mediating mechanisms and by describing the contingent effect of environmental dynamism on the relationship between BDA capability and performance. The results provide new insights into how managers deploy BDA to achieve superior performance in a dynamic environment.

Recent advancements in hyperspectral images classification (HIC) rely on high-quality annotations and thus inevitably suffer from noisy labels. To address the negative effects of noisy labels, some methods employ neighborhood samples to select clean samples and demonstrate promising results. However, they typically rely on robust feature extraction and remain limited under high noise ratios. To overcome the limitations, we propose a novel robust sample selection and correction method based on robust contrastive learning and neighborhood feature modeling. The proposed RSC adopts a dual-branch spectral-spatial network combining spatial and channel-based residual attention modules to extract robust feature. Furthermore, unsupervised contrastive learning at both feature and logit-level are introduced to bolster the feature extractor. Finally, a clean sample selection strategy based on neighborhood consistency in feature space and relabelling scheme by the maximum confidence are integrated to resist the noisy labels. Extensive experiments conducted on publicly available hyperspectral datasets, including Houston and Indian Pines, demonstrate the superior performance of the proposed method, particularly in high noise ratios, where substantial improvements in classification accuracy are observed. The code is available at https://github.com/kovelxyz/RSC.

An efficient robust watermarking method should be resistant to various distortions, including distortions from image processing and geometric attacks. Geometric attacks are significant challenges for watermarking methods because they destroy the synchronization of the watermark between the embedding side and extracting side. It is a considerable challenge to accomplish watermark synchronization for watermarking methods. To address this challenge, a novel robust watermarking method with synchronization is proposed. At the embedding side, the watermark and the template are embedded to generate the watermarked image. If the watermarked image is attacked, the watermark and template are also distorted. At the extracting side, a template enhanced-extracted network is proposed to achieve watermark synchronization. The template enhanced-extracted network effectively extracts the distorted template from the distorted image. The template-enhanced subnet can indirectly enhance the strength of the distorted template in the distorted image and improve the accuracy of the template-extracted subnet. The visual quality of the watermarked image is guaranteed because there is no need to embed the template with high strength. Then, the attack factor is predicted based on the distorted template. By leveraging this prediction, correct watermark extraction with synchronization is achieved. The experimental results demonstrate that the proposed watermarking method with synchronization yields excellent robustness under image processing, geometric attacks and combined attacks.

The purpose of robust image watermarking is to embed a watermark into a carrier image in an invisible form and extract the watermark successfully even under noise interference conditions to achieve copyright confirmation and traceability. Although watermarking methods based on deep learning can improve the robustness by adding a noise simulation layer, few theoretical analyses of the codec structure have been conducted. Theoretical explainability is the theoretical basis for developing a network architecture, which plays a guiding role in network development. On the basis of the interpretability of convolutional networks, this paper analyzes the mathematical process of embedding and extracting watermarks in codecs and proposes a novel watermarking framework based on multi-layer watermark feature fusion. Specifically, the encoder can be a convolutional network structure of arbitrary depth, whereas the decoder needs only to adopt its corresponding deconvolution structure. To improve the quality and robustness of the generated watermarked image, the watermark is associated with an arbitrary layer feature space in the decoder. In the decoder, the network quickly converges to each original encoding feature space through the deconvolution structure, thus decoupling the watermark features. Finally, the watermark is extracted via the automatic fusion of multi-layer watermark features. The experimental results show that the proposed method is suitable for few-shot learning, and its invisibility, robustness and generalization performance on multiple datasets are significantly better than those of other advanced methods.

Home exercise and self-served gyms allow a larger population to exercise regularly without the cost of hiring private coaches. In absence of professional guidance, however, exercisers can suffer from injuries to muscles and joints. High-precision, affordable arm tracking with commercial, off-the-shelf (COTS) wearable devices has become an urgent need to prevent workout injuries and improve exercise performance. Recent studies with inertial measurement units (IMUs) or audio signals are neither computationally feasible for real-time motion tracking with satisfactory accuracy using COTS devices nor practically usable due to the interference with noisy ambient environments. In this paper, we propose UltraMotion, a real-time, high-precision ultrasonic arm motion tracking system designed for practical use. UltraMotion performs point cloud queries based on hidden Markov models (HMMs), a novel ultrasonic acoustic ranging method, and an extended Kalman filter (EKF) to predict the locations of all three arm joints, making it the first system offering shoulder locations. Experimental results with only a smartphone and a smartwatch demonstrate the effectiveness of UltraMotion in tracking shoulder, elbow, and wrist locations with impressively small median errors of 6.4 cm, 7.1 cm, and 8.5 cm in real-world environments, outperforming all previous systems, making UltraMotion an ideal choice for daily exercise.

Image steganographic methods based on encoder-decoder model with end-to-end network architecture recently have been proposed. However, in steganographic applications, the feature map (called stego matrix) generated by the encoder needs to be rounded as a real stego image for the receiver. The loss of precision by rounding stego matrix leads to the decline in the accuracy of extracted secret messages. The challenge of using end-to-end network to preserve robustness against rounding operation is that it is non-differentiable. In this paper, we propose an anti-rounding image steganography method with separable fine-tuning network architecture which includes the joint training stage (JT-stage) and the separable fine-tuning stage (SF-stage). Firstly, in JT-stage, an embedded generator and a stego matrix extractor are jointly learned without rounding operation. Utilizing concatenation in embedded generator can realistically fuse cover image and secret messages. And the multi-scale fusion block and residual dense block in stego matrix extractor can make secret messages more correctly decoded. Moreover, the discriminator is constructed by generative adversarial nets (GAN) in JT-stage to effectively improve the authenticity and steganalysis security. Then, in SF-stage, the embedded generator is frozen, and the stego matrix is obtained and rounded as a stego image. A stego image extractor is constructed by fine-tuning the layers of the stego matrix extractor to improve the accuracy of message extraction. As the loss will not backpropagate in the embedded generator, the non-differentiability of rounding operation can be offset. Experiments show that the proposed separation fine-tuning network is robust to rounding operation, and effectively reduces the degradation of the image quality and steganalysis performance.

Traditional image fusion focuses on selecting an effective decomposition approach to extract representative features from the source image and attempts to find appropriate fusion rules to merge extracted features respectively. However, the existing image decomposition tools are mostly based on kernels or global energy-optimized functions limiting the performance of the wide range of image contents. This paper proposes a novel infrared and visible image fusion method based on deep decomposition network and saliency analysis (named DDNSA). First, the modified residual dense network (MRDN) is trained with a publicly available dataset to learn the decomposition process. Second, the structure and texture features of source images are separated by the trained decomposition network. Then, according to the characteristics of the above features, we construct the combination of local and global saliency maps by using stacked sparse autoencoder and visual saliency mechanism to fuse the structural features. Besides, we propose a bi-direction edge-strength fusion strategy for merging the texture features. Finally, the resultant image is reconstructed by combining the fused structure and texture features. The experimental results confirm that our proposed method outperforms the state-of-the-art methods in both visual perception and objective evaluation.

In this paper, based on the comprehensive information of companies, 612 characteristic parameters are extracted and mined, and two prediction models of the categories of lawsuits are established. The first model is the combinatorial prediction model, which transforms the classification problem into a single-category regression problem. After the Laplace Smoothing treatment of the training label, LightGBM model was used for the 5-fold cross-validation for each of the categories. The Top 1 and Top 2 accuracy of the final combined model was 40.868% and 21.826%, respectively. The second model is Artificial Neural Network (ANN) model, which directly treats the problem as a classification problem. The ANN model with five layers is used to classify and predict the categories of lawsuits, and its Top 1 accuracy is 40.803%, and Top 2 accuracy is 21.243%. Although the accuracy is not ideal, but the method is feasible and can be used for reference. Finally, this paper analyzes the categories of misclassified lawsuits in detail.