Unsupervised domain adaptive object detection methods enhance model robustness in the target domain without requiring target-domain annotations. Despite notable progress, existing methods face two major challenges: 1) insufficient and inefficient learning of holistic feature consistency due to cumbersome pixel-level style matching and semantic discrepancy elimination between domains as well as the overlooking of their collaborative effect; and 2) unreliable learning of category feature compactness caused by poor-quality target-domain samples, inaccurate pseudo-labels and noisy cross-domain contrast paradigms. To address these challenges, we propose a novel Semantic Consistency and Compactness Learning (SCCL) network. For consistency learning, we introduce a Visual Adaptation-guided Semantic Alignment (VSA) module that achieves style matching through simple feature adaptation and incorporates a novel adversarial-free self-supervised method for feature disentanglement. The collaboration between these two aspects enables sufficient and efficient consistency learning. For reliable compactness learning, we develop a plug-and-play Instance Center-Contrastive (ICC) head that, for the first time, comprehensively addresses all three potential causes of unreliable learning through three integrated innovations, concerning sample pseudo-label quality enhancement, reliable sample storage and updating, and a robust sample contrast paradigm. Besides, the mutual reinforcement effect of VSA and ICC simultaneously enhances feature transferability and discriminability. Extensive experiments across four UDA object detection benchmarks with two baselines show that SCCL achieves superior adaptability and robustness. Code will be available at https://github.com/TooZE23/SCCL.

In cloud-scale systems, failures are the norm. A distributed computing cluster exhibits hundreds of machine failures and thousands of disk failures; software bugs and misconfigurations are reported to be more frequent. The demand for autonomous, AI-driven reliability engineering continues to grow, as existing human-in-the-loop practices can hardly keep up with the scale of modern clouds. This paper presents STRATUS, an LLM-based multi-agent system for realizing autonomous Site Reliability Engineering (SRE) of cloud services. STRATUS consists of multiple specialized agents (e.g., for failure detection, diagnosis, mitigation), organized in a state machine to assist system-level safety reasoning and enforcement. We formalize a key safety specification of agentic SRE systems like STRATUS, termed Transactional No-Regression (TNR), which enables safe exploration and iteration. We show that TNR can effectively improve autonomous failure mitigation. STRATUS significantly outperforms state-of-the-art SRE agents in terms of success rate of failure mitigation problems in AIOpsLab and ITBench (two SRE benchmark suites), by at least 1.5 times across various models. STRATUS shows a promising path toward practical deployment of agentic systems for cloud reliability.

Underwater imaging in turbid environments presents significant challenges for industrial applications due to optical distortions that severely degrade image quality. This article addresses the fundamental information deficit in traditional single-image restoration methods by introducing the first comprehensive Light Field Underwater Image Dataset, comprising 1356 images captured across diverse environments with 1820× 720× 14× 14× 3 resolution. We develop a novel physics-based image formation model that extends scattering principles to the 4-D light field domain, incorporating water attenuation coefficients and scattering properties while accounting for light field camera characteristics. Our model features three key components: an ambient underwater optical constant, a phase function for angular scattering, and a pixel position modulation function for spatial variations in backscatter. Based on this model, we propose a restoration algorithm that leverages complementary information across subaperture views. Experimental results demonstrate that our approach significantly outperforms state-of-the-art underwater image enhancement techniques across multiple metrics and diverse underwater scenes, establishing a promising new direction for underwater imaging applications.

Polarization provides valuable physical information, making it beneficial for various computer vision tasks. However, haze reduces both the color and polarization information of a scene. While existing single-image dehazing methods can restore color information, they are poor at recovering polarization information. Furthermore, current polarization-based dehazing approaches neglect the physical mechanisms of polarization degradation, resulting in inaccurate reconstruction of polarization information. In this paper, we propose a novel polarization dehazing algorithm, along with a polarization degradation model, to accurately recover both polarization and color information. First, we combine two key characteristics (the polarization achromatism prior and polarization attenuation prior) with the polarization degradation model to precisely reconstruct the scene's polarization. Then, we utilize the reconstructed polarization information to recover the color information of the scene. Finally, a multi-scale fusion optimization framework is introduced to further enhance the image quality. Our method shows excellent performance on both real-world indoor and outdoor polarized images, outperforming existing dehazing algorithms in both objective evaluation metrics and subjective visual assessment.

Retry---the re-execution of a task on failure---is a common mechanism to enable resilient software systems. Yet, despite its commonality and long history, retry remains difficult to implement and test.

While significant progress has been made in recent years in the field of salient object detection, there are still limitations in heterogeneous modality fusion and salient feature integrity learning. The former is primarily attributed to a paucity of attention from researchers to the fusion of cross-scale information between different modalities during processing multi-modal heterogeneous data, coupled with an absence of methods for adaptive control of their respective contributions. The latter constraint stems from the shortcomings in existing approaches concerning the prediction of salient region’s integrity. To address these problems, we propose a Heterogeneous Fusion and Integrity Learning Network for RGB-D Salient Object Detection (HFIL-Net). In response to the first challenge, we design an Advanced Semantic Guidance Aggregation (ASGA) module, which utilizes three fusion blocks to achieve the aggregation of three types of information: within-scale cross-modal, within-modal cross-scale, and cross-modal cross-scale. In addition, we embed the local fusion factor matrices in the ASGA module and utilize the global fusion factor matrices in the Multi-modal Information Adaptive Fusion module to control the contributions adaptively from different perspectives during the fusion process. For the second issue, we introduce the Feature Integrity Learning and Refinement Module. It leverages the idea of ”part-whole” relationships from capsule networks to learn feature integrity and further refine the learned features through attention mechanisms. Extensive experimental results demonstrate that our proposed HFIL-Net outperforms over 17 state-of-the-art detection methods in testing across seven challenging standard datasets. Codes and results are available on https://github.com/BojueGao/HFIL-Net .

The past decade has witnessed great progress in RGB-D salient object detection (SOD). However, there are two bottlenecks that limit its further development. The first one is low-quality depth maps. Most existing methods directly use raw depth maps to perform detection, but low-quality depth images can bring negative impacts to the detection performance. Hence, it is not desirable to utilize depth maps indiscriminately. The other one is how to effectively predict salient maps with clear boundary and complete salient region. To address these problems, an Attention-Guided Multi-Modality Interaction Network (AMINet) is proposed. First, we propose a new quality enhancement strategy for unreliable depth images, named D epth E nhancement M odule ( DEM ). With respect to the second issue, we propose C ross- M odality A ttention M odule ( CMAM ) to rapidly locate salient region. The B oundary- A ware M odule ( BAM ) is designed to utilize high-level feature to guide the low-level feature generation in a top-down way to make up for the dilution of the boundary. To further improve the accuracy, we propose A trous R efined B lock ( ARB ) to adaptively compensate for the shortcoming of atrous convolution. By integrating these interactive modules, features from depth and RGB streams can be refined efficiently, which consequently boosts the detection performance. Experimental results demonstrate the proposed AMINet exceeds state-of-the-art (SOTA) methods on several public RGB-D datasets.

It is well known that valuable information is contained in the natural language components of software systems, like comments and manual, and such information can be used to improve system performance and reliability. Past research has attempted to extract such information through task-specific machine learning models and tool chains. Here, we investigate a general, one-model-fit-all solution through a state-of-the-art large language model (e.g., the GPT series). Our investigation covers three representative tasks: extracting locking rules from comments, synthesizing exception predicates from comments, and identifying performance-related configurations; it reveals challenges and opportunities in applying large language models to system maintenance tasks.