Unpaired image restoration (UIR) is a significant task due to the difficulty of acquiring paired degraded/clear images with identical backgrounds. In this paper, we propose a novel UIR method based on the assumption that an image contains both degradation-related features, which affect the level of degradation, and degradation-unrelated features, such as texture and semantic information. Our method aims to ensure that the degradation-related features of the restoration result closely resemble those of the clear image, while the degradation-unrelated features align with the input degraded image. Specifically, we introduce a Feature Orthogonalization Module optimized on Stiefel manifold to decouple image features, ensuring feature uncorrelation. A task-driven Depth-wise Feature Classifier is proposed to assign weights to uncorrelated features based on their relevance to degradation prediction. To avoid the dependence of the training process on the quality of the clear image in a single pair of input data, we propose to maintain several degradation-related proxies describing the degradation level of clear images to enhance the model's robustness. Finally, a weighted PatchNCE loss is introduced to pull degradation-related features in the output image toward those of clear images, while bringing degradation-unrelated features close to those of the degraded input.

Traditional photography composition approaches are dominated by 2D cropping-based methods. However, these methods fall short when scenes contain poorly arranged subjects. Professional photographers often employ perspective adjustment as a form of 3D recomposition, modifying the projected 2D relationships between subjects while maintaining their actual spatial positions to achieve better compositional balance. Inspired by this artistic practice, we propose photography perspective composition (PPC), extending beyond traditional cropping-based methods. However, implementing the PPC faces significant challenges: the scarcity of perspective transformation datasets and undefined assessment criteria for perspective quality. To address these challenges, we present three key contributions: (1) An automated framework for building PPC datasets through expert photographs. (2) A video generation approach that demonstrates the transformation process from less favorable to aesthetically enhanced perspectives. (3) A perspective quality assessment (PQA) model constructed based on human performance. Our approach is concise and requires no additional prompt instructions or camera trajectories, helping and guiding ordinary users to enhance their composition skills.

Graphical User Interface (GUI) agents have made substantial strides in understanding and executing user instructions across diverse platforms. Yet, grounding these instructions to precise interface elements remains challenging—especially in complex, high-resolution, professional environments. Traditional supervised fine-tuning (SFT) methods often require large volumes of diverse data and exhibit weak generalization. To overcome these limitations, we introduce a reinforcement learning (RL)-based framework that incorporates three core strategies: (1) seed data curation to ensure high-quality training samples, (2) a dense policy gradient that provides continuous feedback based on prediction accuracy, and (3) a self-evolutionary reinforcement finetuning mechanism that iteratively refines the model using attention maps. With only 3k training samples, our 7B-parameter model achieves state-of-the-art results among similarly sized models on three grounding benchmarks. Notably, it attains 47.3\% accuracy on the ScreenSpot-Pro dataset—outperforming much larger models, such as UI-TARS-72B, by a margin of 24.2\%. These findings underscore the effectiveness of RL-based approaches in enhancing GUI agent performance, particularly in high-resolution, complex environments.

Early smoke segmentation (ESS) enables the accurate identification of smoke sources, facilitating the prompt extinguishing of fires and preventing large-scale gas leaks. But ESS poses greater challenges than conventional object and regular smoke segmentation due to its small scale and transparent appearance, which can result in high miss detection rate and low precision. To address these issues, a Focus and Separation Network (FoSp) is proposed. We first introduce a Focus module employing bidirectional cascade which guides low-resolution and high-resolution features towards mid-resolution to locate and determine the scope of smoke, reducing the miss detection rate. Next, we propose a Separation module that separates smoke images into a pure smoke foreground and a smoke-free background, enhancing the contrast between smoke and background fundamentally, improving segmentation precision. Finally, a Domain Fusion module is developed to integrate the distinctive features of the two modules which can balance recall and precision to achieve high F_beta. Futhermore, to promote the development of ESS, we introduce a high-quality real-world dataset called SmokeSeg, which contains more small and transparent smoke than the existing datasets. Experimental results show that our model achieves the best performance on three available smoke segmentation datasets: SYN70K (mIoU: 83.00%), SMOKE5K (F_beta: 81.6%) and SmokeSeg (F_beta: 72.05%). The code can be found at https://github.com/LujianYao/FoSp.

Smoke segmentation is of great importance in precisely identifying the smoke location, enabling timely fire rescue and gas leak detection. However, due to the visual diversity and blurry edges of the non-grid smoke, noisy labels are almost inevitable in large-scale pixel-level smoke datasets. Noisy labels significantly impact the robustness of the model and may lead to serious accidents. Nevertheless, currently, there are no specific methods for addressing noisy labels in smoke segmentation. Smoke differs from regular objects as its transparency varies, causing inconsistent features in the noisy labels. In this paper, we propose a conditional sample weighting (CoSW). CoSW utilizes a multi-prototype framework, where prototypes serve as prior information to apply different weighting criteria to the different feature clusters. A novel regularized within-prototype entropy (RWE) is introduced to achieve CoSW and stable prototype update. The experiments show that our approach achieves SOTA performance on both real-world and synthetic noisy smoke segmentation datasets.

In the image dehazing task, the haze density is a key feature that affects the performance of dehazing methods. The haze density difference, which has rarely been utilized in previous methods, can guide networks to perceive different global densities and focus on local areas with high density or that are difficult to dehaze. In this paper, we propose a density-aware dehazing method named the Density Feature Refinement Network (DFR-Net), which extracts haze density features from density differences and leverages density differences to refine density features. In DFR-Net, we first generate a proposal image that has a lower overall density than the hazy input, resulting in global density differences. Additionally, the dehazing residual of the proposal image reflects the level of dehazing performance and provides local density differences that indicate localized hard dehazing or high-density areas. Subsequently, we introduce a Global Branch (GB) and a Local Branch (LB) to achieve density awareness. In GB, we use Siamese networks for feature extraction of hazy inputs and proposal images, and we propose a Global Density Feature Refinement (GDFR) module that can refine features by pushing features with different global densities further away. In LB, we explore local density features from the dehazing residuals between hazy inputs and proposal images and introduce an Intermediate Dehazing Residual Feedforward (IDRF) module to update local features and pull them close to clear image features. Sufficient experiments demonstrate that the proposed method outperforms state-of-the-art methods on various datasets.

In the traditional fault diagnosis task, it is difficult to collect training samples to exhaust all fault classes. There are massive target faults that cannot be collected in advance, which may restrict the performance of fault diagnosis methods. In this article, a novel method named semantic-consistent embedding (SCE) is proposed for zero-shot industrial fault diagnosis. SCE tries to classify unseen class faults only by using seen class faults for training. The fault samples and their human-specified attribute vectors are embedded into a semantic-consistent space and then reconstructed from that space. A specificBarlow matrixis designed to measure the consistency between the embedding of fault samples and the embedding of attribute vectors. The diagonal elements and the off-diagonal elements of the Barlow matrix encode the within-dimension consistency and between-dimension consistency of the cross-modal embeddings, respectively. Through optimizing the Barlow matrix to an identity matrix, SCE learns a significant space where the cross-modal embeddings have consistent representation while reducing the redundant components. Extensive experiments show that SCE gets significant superiority on the three-phase transmission system (26.9% gains) and the Tennessee Eastman process (15.5% gains). Moreover, SCE even gets competitive results with supervised learning methods.