Severe weather restoration models often face the simultaneous interaction of multiple degradations in real-world scenarios. Existing approaches typically handle single or composite degradations based on scene descriptors derived from text or image embeddings. However, due to the varying proportions of different degradations within an image, these scene descriptors may not accurately differentiate between degradations, leading to suboptimal restoration in practical applications. To address this issue, we propose a novel Transformer-based restoration framework, AllRestorer, for dealing with four physical severe weather impairments: low-light, haze, rain, and snow. In AllRestorer, we enable the model to adaptively consider all weather impairments, thereby avoiding errors from scene descriptor misdirection. Specifically, we introduce the All-in-One Transformer Block (AiOTB), the core innovation of which is the ability to adaptively handle multiple degradations in a single image, beyond the limitation of existing Transformers that can only handle one type of degradation at a time. To accurately address different variations potentially present within the same type of degradation and minimize ambiguity, AiOTB utilizes a Composite Scene Embedding consisting of both image and text embeddings to define the degradation. Moreover, AiOTB includes an adaptive weight for each degradation, allowing for precise control of the restoration intensity. By leveraging AiOTB, AllRestorer avoids misdirection caused by inaccurate scene descriptors, achieving a 5.00 dB increase in PSNR compared to the baseline on the CDD-11 dataset.

This paper proposes the first pure Transformer structure inversion network called SwinStyleformer, which can compensate for the shortcomings of the CNNs inversion framework by handling long-range dependencies and learning the global structure of objects. Experiments found that the inversion network with the Transformer backbone could not successfully invert the image. The above phenomena arise from the differences between CNNs and Transformers, such as the self-attention weights favoring image structure ignoring image details compared to convolution, the lack of multi-scale properties of Transformer, and the distribution differences between the latent code extracted by the Transformer and the StyleGAN style vector. To address these differences, we employ the Swin Transformer with a smaller window size as the backbone of the SwinStyleformer to enhance the local detail of the inversion image. Meanwhile, we design a Transformer block based on learnable queries. Compared to the self-attention transformer block, the Transformer block based on learnable queries provides greater adaptability and flexibility, enabling the model to update the attention weights according to specific tasks. Thus, the inversion focus is not limited to the image structure. To further introduce multi-scale properties, we design multi-scale connections in the extraction of feature maps. Multi-scale connections allow the model to gain a comprehensive understanding of the image to avoid loss of detail due to global modeling. Moreover, we propose an inversion discriminator and distribution alignment loss to minimize the distribution differences. Based on the above designs, our SwinStyleformer successfully solves the Transformer’s inversion failure issue and demonstrates SOTA performance in image inversion and several related vision tasks.

Model-based Test Case Prioritization utilizing similarity metrics has proved effective in software testing. However, the utility of similarity metrics in it varies with test scenarios, hindering its universal effectiveness and performance optimization . To tackle this problem, we propose a Diversity-driven Learn-to-rank model-based TCP approach, named DLTCP, for optimizing early fault detection performance. Our method first employs the whale optimization algorithm to search for a suitable set of similarity metrics from a pool of existing candidates. This search process determines which metrics should be used. According to each selected metric, test cases are then prioritized. The resulting test case rankings are used as the training data for DLTCP. Finally, the proposed method incorporates random forest to train a ranking model for test case prioritization. As such, it can fuse multiple similarity metrics to improve the TCP performance. We conduct extensive experiments to evaluate our method’s performance using the average percentage fault detected (APFD) as metric. The experimental results show that DLTCP achieve an average APFD value of 0.953 for seven classic benchmark models , which is 11.37% higher than that of the state-of-the-art algorithms. It can well select a set of similarity metrics for effective fusion, demonstrating competitive performance in early fault detection.

Subspace clustering has attracted much attention because of its ability to group unlabeled high-dimensional data into multiple subspaces. Existing graph-based subspace clustering methods focus on either the sparsity of data affinity or the low rank of data affinity. Thus, the quality of data affinity plays an essential role in the performance of subspace clustering. However, the real-world data are generally high-dimensional, complex, and heterogeneous multi-source data, so that the data affinity learned by these methods cannot be completely dependent. Moreover, since these approaches always ignore the intrinsic structure of data, their grouping effect is relatively low. In this paper, we propose a novel unsupervised algorithm, called Structure-Aware Subspace Clustering (SASC), to address the above issues. SASC considers local and global correlation structures simultaneously to capture the intrinsic structure. Further, it integrates the captured structure into representation learning to gain a relatively precise data affinity. It is powerful to promote an all-around grouping effect and enhances the robustness and applicability of subspace clustering. Experiments on various benchmark datasets, including bioinformatics, handwritten digit, object image, and speech signal, demonstrate the effectiveness of the proposed algorithm.

The goal of semisupervised kernel matrix learning (SS-KML) is to learn a kernel matrix on all the given samples on which just a little supervised information, such as class label or pairwise constraint, is provided. Despite extensive research, the performance of SS-KML still leaves some space for improvement in terms of effectiveness and efficiency. For example, a recent pairwise constraints propagation (PCP) algorithm has formulated SS-KML into a semidefinite programming (SDP) problem, but its computation is very expensive, which undoubtedly restricts PCPs scalability in practice. In this paper, a novel algorithm, called kernel propagation (KP), is proposed to improve the comprehensive performance in SS-KML. The main idea of KP is first to learn a small-sized sub-kernel matrix (named seed-kernel matrix) and then propagate it into a larger-sized full-kernel matrix. Specifically, the implementation of KP consists of three stages: 1) separate the supervised sample (sub)set X(l) from the full sample set X; 2) learn a seed-kernel matrix on X(l) through solving a small-scale SDP problem; and 3) propagate the learnt seed-kernel matrix into a full-kernel matrix on X . Furthermore, following the idea in KP, we naturally develop two conveniently realizable out-of-sample extensions for KML: one is batch-style extension, and the other is online-style extension. The experiments demonstrate that KP is encouraging in both effectiveness and efficiency compared with three state-of-the-art algorithms and its related out-of-sample extensions are promising too.