Multimodal Recommendation Systems (MRSs) boost traditional user-item interaction-based methods by incorporating multimodal information. However, existing methods ignore the inherent noise brought by (1) noisy semantic priors in multimodal content, and (2) noisy user interactions in history records, therefore diminishing model performance. To fill this gap, we propose to denoise MRSs by jointly EValuating structure Effectiveness and mitigating Noisy links (EVEN). Firstly, for semantic prior noise in multimodal content, EVEN builds item homogeneous consistency and denoises it by evaluating behavior-driven confidence. Secondly, for noise in user interactions, EVEN updates user feedback by denoising observed interactions following implicit contribution evaluation of high-order representations. Thirdly, EVEN performs cross-modal alignment through self-guided structure learning, reinforcing task-specific inter-modal dependency modeling and cross-modal fusion. Through extensive experiments on three widely-used datasets, EVEN achieves an average improvement of 8.95% and 5.90% in recommendation accuracy compared with LGMRec and FREEDOM, respectively, without extending the total training time.

The detection of image tampering, specifically copy detection, is an important problem in many domains such as military, media, and public opinion outlets. Effective means to detect such tampering is crucial in controlling the dissemination of false information. However, a major challenge in achieving high detection accuracy lies in the variability of the scale of the copied targets. To tackle this problem, we introduce an all-encompassing methodology called Cross-Scale Modeling and Alternating Refinement (CANet) to detect the genuine source and tampered region at the pixel level. CANet consists of three modules: the Cross-Scale Similar Region Detection (CS) module, the Edge-Supervised Tamper Region Detection (ET) module, and the Alternating Refinement (AR) module. The CS module extracts coarse similar region features by cross-scale correlation modeling, which can alleviate the scale gap between the source and tampered region. The obtained coarse similar region feature is refined by the AR module, in which we introduce the source and the tampered region as the auxiliary information and employ a two-stage process that sequentially models their global feature representations. The tampered region used in the AR module is obtained from the ET module using edge supervision with a salient edge selection scheme, and the source region is generated by the implicit modeling. We conducted experiments on the USC-ISI, CASIA v2.0, CoMoFoD, and MICC-F220 datasets separately. Results show that our method outperforms the state-of-the-art.

In recent years, there has been a growing demand for the colorization of remote sensing images due to their inherent limitations caused by remote sensors, such as hazy or noisy atmospheric conditions. These factors result in the captured images needing to be clarified. Compared to ordinary images, remote sensing images present unique challenges in color recovery due to their imbalanced spatial distribution of objects. In this article, we propose a novel bidirectional layout-semantic-pixel joint decoupling and embedding network (BDEnet) following the idea of human painting to generate highly saturated color images with strong spatial consistency and object salience. The proposed BDEnet model emulates the process of human painting through a step-by-step approach. It begins by determining the overall tone of a large macroscopic region and progressively refining the local color based on this initial assessment. Specifically, BDEnet incorporates finer-grained semantics and pixel color information into a colored layout that represents a wide range of continuous areas, thereby accomplishing the colorization task. The BDEnet model operates at three scales, namely the layout (macro), semantic (medium), and pixel (micro) scales. It comprises three key modules: the multiscale feature decoupling (MFD) module, the layout-semantic-pixel multigranularity learning (MGL) module, and the semantic-pixel embedding (SPE) module. MFD module effectively reduces redundant noise from the semantic and layout scales by employing scale decoupling. This process ensures the extraction of efficient features essential for MGL. In the MGL module, three branches with different scales are employed to achieve layout division, semantic segmentation, and pixel coloring. To address the issue of insufficient category label guidance in layouts, we propose a novel approach called similar semantic merging (SSM) using a weakly supervised scheme to accomplish layout division. Finally, the SPE module incorporates stable semantic and pixel information into the layout features. This integration results in the generation of color images that exhibit strong spatial consistency, emphasize object salience, and possess high color saturation.

Low-order features based on convolution kernel are easy to be distorted when encountering dramatic view angle transformation and atmospheric scattering in remote sensing (RS) images. To address this concern, this article first proposes to operate semantic segmentation of RS images based on the high-order information, which can represent the relative relationship of low-order features and is robust and stable when suffering feature distortion. Besides, semantic decouples have recently been well researched and have achieved significant improvement in image understanding. Thus, in this article, a high-order semantic decoupling network (HSDN) is proposed to disentangle features by semantics based on high-order features. Specifically, HSDN first represents each pixel by calculating the pixel-level affinity as a high-order feature and then clusters these pixels into different semantics. Afterward, an attention-like mask generation module is designed for both intra-semantic and inter-semantic groups, leading to three kinds of masks, including the semantic decoupling mask (SDM), which utilizes each high-order cluster centroid as a mask to compact features intracluster and expand different interclusters, so as to improve semantic disentangle performance to a better extent; semantic enhancement mask (SEM), which records pixel-level relative correlation within a class to sufficiently exploit high-order features and could enhance feature robustness; and boundary supplementary mask (BSM), which aims to process borderline pixels to reduce cluster errors. Finally, by applying masks on pixels both within classes and on borderlines, semantic decoupled features are generated and concatenated to realize segmentation. The quantitative and qualitative experiments are conducted on two large-scale fine-resolution RS image datasets to demonstrate the significant performance of adopting high-order representation. Besides, we also implement numerous experiments to validate the effectiveness of the proposed semantic decouple framework in dealing with complicated and distortion-prone RS image segmentation tasks.

With the development of deep neural networks, especially generation networks, gray image coloring technology has made great progress. As one of the fields, remote sensing image colorization needs to be solved urgently. This is because remote sensing images cannot obtain clear color images due to the limitations of shooting equipment and transmission equipment. Compared with ordinary images, remote sensing images are characterized by the uneven spatial distribution of objects, therefore, it is a great challenge to ensure the spatial consistency of coloring. To embrace this challenge, we propose a new joint stream DCGAN including a micro stream and a macro stream, in which the latter is set as a prior to constrain the former for colorization. In addition, the Low-level Correlation Feature Extraction (LCFE) module is proposed to obtain the salient shallow detail feature with global correlation, which is used to enhance the global constraints as well as supplement the low-level information to the micro stream. What's more, we propose the Gated Selection (GSM) module by selecting useful information using a gated scheme to fuse features from two streams appropriately. Comprehensive comparison and ablation experiments are implemented and verify the proposed method performs surpasses other methods in both qualitative and quantitative metrics.