Person re-identification (re-ID) under various occlusions has been a long-standing challenge as person images with different types of occlusions often suffer from misalignment in image matching and ranking. Most existing methods tackle this challenge by aligning spatial features of body parts according to external semantic cues or feature similarities but this alignment approach is complicated and sensitive to noises. We design DRL-Net, a disentangled representation learning network that handles occluded re-ID without requiring strict person image alignment or any additional supervision. Leveraging transformer architectures, DRL-Net achieves alignment-free re-ID via global reasoning of local features of occluded person images. It measures image similarity by automatically disentangling the representation of undefined semantic components, e.g., human body parts or obstacles, under the guidance of semantic preference object queries in the transformer. In addition, we design a decorrelation constraint in the transformer decoder and impose it over object queries for better focus on different semantic components. To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features. Extensive experiments over occluded and holistic re-ID benchmarks (Occluded-DukeMTMC, Market1501 and DukeMTMC) show that the DRL-Net achieves superior re-ID performance consistently and outperforms the state-of-the-art by large margins for Occluded-DukeMTMC. Code is available at https://github.com/Anonymous-release-code/DRL-Net.

As large language models (LLMs) grow increasingly sophisticated and pervasive, their application to various Artificial Intelligence for IT Operations (AIOps) tasks has garnered significant attention. However, a comprehensive understanding of the impact, potential, and limitations of LLMs in AIOps remains in its infancy. To address this gap, we conducted a detailed survey of LLM4AIOps, focusing on how LLMs can optimize processes and improve outcomes in this domain. We analyzed 183 research articles published between January 2020 and December 2024 to answer four key research questions (RQs). In RQ1, we examine the diverse failure data sources utilized, including advanced LLM-based processing techniques for legacy data and the incorporation of new data sources enabled by LLMs. RQ2 explores the evolution of AIOps tasks, highlighting the emergence of novel tasks and the publication trends across these tasks. RQ3 investigates the various LLM-based methods applied to address AIOps challenges. Finally, RQ4 reviews evaluation methodologies tailored to assess LLM-integrated AIOps approaches. Based on our findings, we discuss the state-of-the-art advancements and trends, identify gaps in existing research, and propose promising directions for future exploration.

Pedestrian attribute recognition (PAR), which aims to identify attributes of the pedestrians captured in video surveillance, is a challenging task due to the poor quality of images and diverse spatial distribution among attributes. Existing methods usually model PAR as a multi-label classification problem and manually map attributes to an ordered list corresponding to the outputs of classifiers or sequential models. However, the inherent textual information among attribute annotations is largely neglected in these visual-only methods. In this paper, we first alleviate this issue by proposing a novel visual-textual baseline (VTB) for PAR which introduces an additional textual modality to explore the textual semantic correlations from attribute annotations by pre-trained textual encoders instead of human definitions. VTB encodes pedestrian images and attribute annotations into visual and textual features respectively, interacts with information across modalities, and predicts recognition results independently to remove the influence of attribute orders. Furthermore, we introduce transformer encoder as the cross-modal fusion module in VTB for sufficient intra-modal and cross-modal correlations exploration. Our method achieves superior performance over most existing visual-only methods on two widely used datasets including RAP and PA-100K, demonstrating the effectiveness of utilizing textual modality to PAR. Our method is expected to serve as a multimodal PAR baseline and inspire new insights for multimodal fusion in future PAR research. Our code is available at https://github.com/cxh0519/VTB .

This paper proposes a Semi-Attention Partition (SAP) method to learn well-aligned part features for occluded person re-identification (re-ID). Currently, the mainstream methods employ either external semantic partition or attention-based partition, and the latter manner is usually better than the former one. Under this background, this paper explores a potential that the weak semantic partition can be a good teacher for the strong attention-based partition. In other words, the attention-based student can substantially surpass its noisy semantic-based teacher, contradicting the common sense that the student usually achieves inferior (or comparable) accuracy. A key to this effect is: the proposed SAP encourages the attention-based partition of the (transformer) student to be partially consistent with the semantic-based teacher partition through knowledge distillation, yielding the so-called semi-attention. Such partial consistency allows the student to have both consistency and reasonable conflict with the noisy teacher. More specifically, on the one hand, the attention is guided by the semantic partition from the teacher. On the other hand, the attention mechanism itself still has some degree of freedom to comply with the inherent similarity between different patches, thus gaining resistance against noisy supervision. Moreover, we integrate a battery of well-engineered designs into SAP to reinforce their cooperation (e.g., multiple forms of teacher-student consistency), as well as to promote reasonable conflict (e.g., mutual absorbing partition refinement and a supervision signal dropout strategy). Experimental results confirm that the transformer student achieves substantial improvement after this semi-attention learning scheme, and produces new state-of-the-art accuracy on several standard re-ID benchmarks.

To ensure the stability and reliability of microservice systems, timely and accurate anomaly detection is of utmost importance. Recently, considering the lack of labels in real-world scenarios and the collaborative and complementary relationships of multi-modal data in reflecting system anomalies, unsupervised multi-modal anomaly methods have been proposed. However, existing methods face challenges in effectively distinguishing normal hard samples (they are normal but hard to classify correctly) from anomalies. This is mainly caused by two aspects. Firstly, the hard sample patterns are complex. Secondly, the convergence speed is inconsistent between hard and simple samples. To overcome these issues, we propose an unsupervised adversarial contrastive multi-modal anomaly detection method (UACAD). We utilize contrastive learning to help learn the complex patterns of hard samples and enlarge the distance between hard and anomaly samples. Meanwhile, the adversarial framework automatically identifies hard samples and fine-grained adjusts the training weights to each modality part of these hard samples. In this case, The hard sample problems of two aspects can be alleviated. We extensively evaluate UAC-AD on two open-source simulated datasets and a real industrial dataset from a large communication company. Extensive experimental results demonstrate the effectiveness of our approach in anomaly detection. We also release the code and dataset for replication and future research

Distributed databases are fundamental infrastructures of today's large-scale software systems such as cloud systems. Detecting anomalies in distributed databases is essential for maintaining software availability. Existing approaches, predominantly developed using Loghub-a comprehensive collection of log datasets from various systems-lack datasets specifically tailored to distributed databases, which exhibit unique anomalies. Additionally, there's a notable absence of datasets encompassing multi-anomaly, multi-node logs. Consequently, models built upon these datasets, primarily designed for standalone systems, are inadequate for distributed databases, and the prevalent method of deeming an entire cluster anomalous based on irregularities in a single node leads to a high false-positive rate. This paper addresses the unique anomalies and multivariate nature of logs in distributed databases. We expose the first open-sourced, comprehensive dataset with multivariate logs from distributed databases. Utilizing this dataset, we conduct an extensive study to identify multiple database anomalies and to assess the effectiveness of state-of-the-art anomaly detection using multivariate log data. Our findings reveal that relying solely on logs from a single node is insufficient for accurate anomaly detection on distributed database. Leveraging these insights, we propose MultiLog, an innovative multivariate log-based anomaly detection approach tailored for distributed databases. Our experiments, based on this novel dataset, demonstrate MultiLog's superiority, outperforming existing state-of-the-art methods by approximately 12%.